A survey on Data Mining approaches for Healthcare
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paper_title: Data Mining Concepts and Techniques

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Understand the need for analyses of large, complex, information-rich data sets. Identify the goals and primary tasks of the data-mining process. Describe the roots of data-mining technology. Recognize the iterative character of a data-mining process and specify its basic steps. Explain the influence of data quality on a data-mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers. In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining, which produces new, nontrivial information based on the available data set.
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paper_title: Principles of Data Mining

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The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
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paper_title: From Data Mining to Knowledge Discovery in Databases

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The data that most organizations possess is considered a valuable asset. To really benefit from it, organizations must be able to analyze these data efficiently and effectively. This activity is nowadays referred to as business intelligence and one class of algorithms used within this activity are called data mining methods. There are some famous success stories about knowledge discovered via data mining but there is also a lot of disappointment so far. The paper argues that one of the reasons for some failures to produce better results with data mining is the reliance on transactional and master data only. It points to other types of data that can enrich transactional data and help in this way to produce more interesting and more rewarding data patterns.
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paper_title: Modeling and design of evolutionary neural network for heart disease detection

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This research is having purpose to find the alternatives to the solution of complex medical diagnosis in detection of heart disease where human knowledge is apprehended in a general fashion. Successful application examples shown previously that human diagnostic capabilities are significantly worse than the neural diagnostic system. This paper describes a new system for detection of heart disease based on feed forward neural network architecture and genetic algorithm. Hybridization has applied to train the neural network using Genetic algorithm and proved experimentally, proposed learning is more stable compare to back propagation. Detail analysis has given with respect to genetic algorithm behavior and its relationship with learning performance of neural network. Affect of tournament selection has analyzed to get more detailed knowledge what is happening internally. With the proposed system we hope that, design of diagnosis system for heart disease detection will become easy, cost effective, reliable and efficient.
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paper_title: An incremental decision tree learning methodology regarding attributes in medical data mining

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Decision tree is one kind of inductive learning algorithms that offers an efficient and practical method for generalizing classification rules from previous concrete cases that already solved by domain experts. It is considered attractive for many real-life applications, mostly due to its interpretability. Recently, many researches have been reported to endow decision trees with incremental learning ability, which is able to address the learning task with a stream of training instances. However, there are few literatures discussing the algorithms with incremental learning ability regarding the new attributes. In this paper, i+Learning (Intelligent, Incremental and Interactive Learning) theory is proposed to complement the traditional incremental decision tree learning algorithms by concerning new available attributes in addition to the new incoming instances. The experimental results reveal that i+Learning method offers the promise of making decision trees a more powerful, flexible, accurate and valuable paradigm, especially in medical data mining community.
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paper_title: A Classification Tree for Predicting Recurrent Falling in Community-Dwelling Older Persons

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OBJECTIVES: To develop a classification tree for predicting the risk of recurrent falling in community-dwelling older persons using tree-structured survival analysis (TSSA). DESIGN: A prospective cohort study. SETTING: A community in the Netherlands. PARTICIPANTS: One thousand three hundred sixty-five community-dwelling older persons (>/=65) from the Longitudinal Aging Study Amsterdam (LASA). MEASUREMENTS: In 1995, physical, cognitive, emotional, and social aspects of functioning were assessed. Subsequently, a prospective fall follow-up, specifically on recurrent falls (two falls within 6 months) was conducted for 3 years. RESULTS: The classification tree included 11 end groups differing in risk of recurrent falling based on a minimum of two and a maximum of six predictors. The first split in the tree involved two or more falls versus fewer than two falls in the year preceding the interview. Respondents with two or more falls in the year preceding the interview (n=193) and with at least two functional limitations (n=98) had a 75% risk of becoming a recurrent faller, whereas respondents with fewer than two functional limitations were further divided into a group with regular dizziness (n=11, risk of 68%) and a group with no regular dizziness (n=84, risk of 30%). In respondents with fewer than two falls in the year preceding the interview (n=1,172), the risk of becoming a recurrent faller varied between 9% and 70%. Predictors in this branch of the tree were low performance, low handgrip strength, alcohol use, pain, high level of education, and high level of physical activity. CONCLUSION: This classification tree included 11 end groups differing in the risk of recurrent falling based on specific combinations of a maximum of six easily measurable predictors. The classification tree can identify subjects who are eligible for preventive measures in public health strategies.
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paper_title: Predictive Analysis on Hypertension Treatment using Data Mining Approach in Saudi Arabia

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In the present investigation, the data sets of (Non Communicable Diseases) NCD risk factors a standard report of Saudi Arabia 2005 in collaboration with World Health Organisation have been employed for regression analysis using data mining technique and that leads to the prediction of which treatment contributes more to improvement in hypertension. The Oracle Data Miner (ODM) tool has been used for the analysis of data. The data sets for different age groups in case of blood pressure treatment for hypertension for Male using different modes have been studied. The age group is in between of 15 years to 64 years. Data mining is an appropriate and sufficiently sensitive method to analyze outcomes of which mode of treatment is more effective to which age group. The Oracle data miner predicts the best mode of treatment for each group by which one can analyse the appropriate treatment. The predictions are also been compared with the help of residual plots. The residual graphs have been correlated with the predictions.
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paper_title: Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction

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The successful application of data mining in highly visible fields like e-business, marketing and retail has led to its application in other industries and sectors. Among these sectors just discovering is healthcare. The healthcare environment is still „information rich‟ but „knowledge poor‟. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. This research paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today‟s medical research particularly in Heart Disease Prediction. Number of experiment has been conducted to compare the performance of predictive data mining technique on the same dataset and the outcome reveals that Decision Tree outperforms and some time Bayesian classification is having similar accuracy as of decision tree but other predictive methods like KNN, Neural Networks, Classification based on clustering are not performing well. The second conclusion is that the accuracy of the Decision Tree and Bayesian Classification further improves after applying genetic algorithm to reduce the actual data size to get the optimal subset of attribute sufficient for heart disease prediction.
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paper_title: Healthcare information systems : data mining methods in the creation of a clinical recommender system

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Recommender systems have been extensively studied to present items, such as movies, music and books that are likely of interest to the user. Researchers have indicated that integrated medical information systems are becoming an essential part of the modern healthcare systems. Such systems have evolved to an integrated enterprise-wide system. In particular, such systems are considered as a type of enterprise information systems or ERP system addressing healthcare industry sector needs. As part of efforts, nursing care plan recommender systems can provide clinical decision support, nursing education, clinical quality control, and serve as a complement to existing practice guidelines. We propose to use correlations among nursing diagnoses, outcomes and interventions to create a recommender system for constructing nursing care plans. In the current study, we used nursing diagnosis data to develop the methodology. Our system utilises a prefix-tree structure common in itemset mining to construct a ranked list of suggested care plan items based on previously-entered items. Unlike common commercial systems, our system makes sequential recommendations based on user interaction, modifying a ranked list of suggested items at each step in care plan construction. We rank items based on traditional association-rule measures such as support and confidence, as well as a novel measure that anticipates which selections might improve the quality of future rankings. Since the multi-step nature of our recommendations presents problems for traditional evaluation measures, we also present a new evaluation method based on average ranking position and use it to test the effectiveness of different recommendation strategies.
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paper_title: Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction 1

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Medical science industry has huge amount of data, but unfortunately most of this data is not mined to find out hidden information in data. Advanced data mining techniques can be used to discover hidden pattern in data. Models developed from these techniques will be useful for medical practitioners to take effective decision. In this research paper data mining classification techniques RIPPER classifier, Decision Tree, Artificial neural networks (ANNs), and Support Vector Machine (SVM) are analyzed on cardiovascular disease dataset. Performance of these techniques is compared through sensitivity, specificity, accuracy, error rate, True Positive Rate and False Positive Rate. In our studies 10-fold cross validation method was used to measure the unbiased estimate of these prediction models. As per our results error rates for RIPPER, Decision Tree, ANN and SVM are 02.756, 0.2755, 0.2248 and 0.1588 respectively. Accuracy of RIPPER, Decision Tree, ANN and SVM are 81.08%, 79.05%, 80.06% and 84.12% respectively. Our analysis shows that out of these four classification models SVM predicts cardiovascular disease with least error rate and highest accuracy.
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paper_title: Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis

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The threats to people's health from chronic diseases are always exist and increasing gradually. How to decrease these threats is an important issue in medical treatment. Thus, this paper suggests a model of a chronic diseases prognosis and diagnosis system integrating data mining (DM) and case-based reasoning (CBR). The main processes of the system include: (1) adopting data mining techniques to discover the implicit meaningful rules from health examination data, (2) using the extracted rules for the specific chronic diseases prognosis, (3) employing CBR to support the chronic diseases diagnosis and treatments, and (4) expanding these processes to work within a system for the convenience of chronic diseases knowledge creating, organizing, refining, and sharing. The experiment data are collected from a professional health examination center, MJ health screening center, and implemented through the system for analysis. The findings are considered as helpful references for doctors and patients in chronic diseases treatments.
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paper_title: Intelligent heart disease prediction system using data mining techniques

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The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not ";mined"; to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naive Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex ";what if"; queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.
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paper_title: Analysis of cancer data: a data mining approach

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Abstract: Even though cancer research has traditionally been clinical and biological in nature, in recent years data driven analytic studies have become a common complement. In medical domains where data and analytics driven research is successfully applied, new and novel research directions are identified to further advance the clinical and biological studies. In this research, we used three popular data mining techniques (decision trees, artificial neural networks and support vector machines) along with the most commonly used statistical analysis technique logistic regression to develop prediction models for prostate cancer survivability. The data set contained around 120 000 records and 77 variables. A k-fold cross-validation methodology was used in model building, evaluation and comparison. The results showed that support vector machines are the most accurate predictor (with a test set accuracy of 92.85%) for this domain, followed by artificial neural networks and decision trees.
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paper_title: Predictive data mining in clinical medicine: Current issues and guidelines

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Background: The widespread availability of new computational methods and tools for data analysis and predictive modeling requires medical informatics researchers and practitioners to systematically select the most appropriate strategy to cope with clinical prediction problems. In particular, the collection of methods known as ‘data mining’ offers methodological and technical solutions to deal with the analysis of medical data and construction of prediction models. A large variety of these methods requires general and simple guidelines that may help practitioners in the appropriate selection of data mining tools, construction and validation of predictive models, along with the dissemination of predictive models within clinical environments. Purpose: The goal of this review is to discuss the extent and role of the research area of predictive data mining and to propose a framework to cope with the problems of constructing, assessing and exploiting data mining models in clinical medicine. Methods: We review the recent relevant work published in the area of predictive data mining in clinical medicine, highlighting critical issues and summarizing the approaches in a set of learned lessons. Results: The paper provides a comprehensive review of the state of the art of predictive data mining in clinical medicine and gives guidelines to carry out data mining studies in this
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paper_title: Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques

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Healthcare industry is generally "information rich", but unfortunately not all the data are mined which is required for discovering hidden patterns & effective decision making. Advanced data mining techniques are used to discover knowledge in database and for medical research, particularly in Heart disease prediction. This paper has analysed prediction systems for Heart disease using more number of input attributes. The system uses medical terms such as sex, blood pressure, cholesterol like 13 attributes to predict the likelihood of patient getting a Heart disease. Until now, 13 attributes are used for prediction. This research paper added two more attributes i.e. obesity and smoking. The data mining classification techniques, namely Decision Trees, Naive Bayes, and Neural Networks are analyzed on Heart disease database. The performance of these techniques is compared, based on accuracy. As per our results accuracy of Neural Networks, Decision Trees, and Naive Bayes are 100%, 99.62%, and 90.74% respectively. Our analysis shows that out of these three classification models Neural Networks predicts Heart disease with highest accuracy.
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paper_title: Multi-Relational Data Mining in Medical Databases

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This paper presents the application of a method for mining data in a multi-relational database that contains some information about patients strucked down by chronic hepatitis. Our approach may be used on any kind of multirelational database and aims at extracting probabilistic tree patterns from a database using Grammatical Inference techniques. We propose to use a representation of the database by trees in order to extract these patterns. Trees provide a natural way to represent structured information taking into account the statistical distribution of the data. In this work we try to show how they can be useful for interpreting knowledge in the medical domain.
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paper_title: Knowledge Based Approach for Diagnosis of Breast Cancer

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This paper presents a novel approach to simulate a Knowledge Based System for diagnosis of Breast Cancer using Soft Computing tools like Artificial Neural Networks (ANNs) and Neuro Fuzzy Systems. The feed-forward neural network has been trained using three ANN algorithms, the Back propagation algorithm (BPA), the Radial Basis Function (RBF) Networks and the Learning Vector Quantization (LVQ) Networks; and also by Adaptive Neuro Fuzzy Inference System (ANFIS). The simulator has been developed using MATLAB and performance is compared by considering the metrics like accuracy of diagnosis, training time, number of neurons, number of epochs etc. The simulation results show that this Knowledge Based Approach can be effectively used for early detection of Breast Cancer to help oncologists to enhance the survival rates significantly.
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paper_title: Data Mining with Decision Trees and Decision Rules

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Abstract This paper describes the use of decision tree and rule induction in data-mining applications. Of methods for classification and regression that have been developed in the fields of pattern recognition, statistics, and machine learning, these are of particular interest for data mining since they utilize symbolic and interpretable representations. Symbolic solutions can provide a high degree of insight into the decision boundaries that exist in the data, and the logic underlying them. This aspect makes these predictive-mining techniques particularly attractive in commercial and industrial data-mining applications. We present here a synopsis of some major state-of-the-art tree and rule mining methodologies, as well as some recent advances.
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paper_title: Predicting breast cancer survivability using fuzzy decision trees for personalized healthcare

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Data analysis systems, intended to assist a physician, are highly desirable to be accurate, human interpretable and balanced, with a degree of confidence associated with final decision. In cancer prognosis, such systems estimate recurrence of disease and predict survival of patient; hence resulting in improved patient management. To develop such a prognostic system, this paper proposes to investigate a hybrid scheme based on fuzzy decision trees, as an efficient alternative to crisp classifiers that are applied independently. Experiments were performed using different combinations of: number of decision tree rules, types of fuzzy membership functions and inference techniques. For this purpose, SEER breast cancer data set (1973–2003), the most comprehensible source of information on cancer incidence in United States, is considered. Performance comparisons suggest that, for cancer prognosis, hybrid fuzzy decision tree classification is more robust and balanced than independently applied crisp classification; moreover it has a potential to adapt for significant performance enhancement.
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paper_title: Decision tree models for characterizing smoking patterns of older adults

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The main objective of the present paper is to characterize smoking behavior among older adults by assessing the psychological distress, physical health status, alcohol use, and demographic variables in relations to the current smoking. We targeted 466 senior American smokers who are 65 years of age or older from the 2006 National Survey on Drug Use and Health (NSDUH, 2006). We employed a decision tree algorithm to conduct classification analysis to find the relationship between the average numbers of cigarette use per day. The results showed that the most important explanatory variable for prediction of the average number of cigarette use per day is the age when first started smoking cigarettes every day, followed by education level, and psychological distress. These results suggest that social workers need to provide more customized and individualized intervention to older adults.
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paper_title: Applying decision tree and neural network to increase quality of dermatologic diagnosis

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Skin diseases are common to children and adults. Many factors influence the onsets of these diseases, and each age group usually has its different symptoms. In the humid, damp, and hot weather conditions of Taiwan, bacteria and molds grow best and fast. Also, exposures to excess amounts of ultraviolet radiations in the sunlight will make skin sensitive, easy to be infected, and possibly cause skin problems. In addition to the external infections, internal sebaceous glands, dead skin, sweats, mixed with dusts and other unwanted secretions can cause other serious skin diseases. Although skin diseases are easier to detect, and diagnosing symptoms and deciding treatment plans are not as complex as other internal diseases, many people often ignore the importance of them. In fact, even a small spot on the skin might cause skin cancer. This study conducted five experiments focusing on six major skin diseases as its research subjects. It uses decision tree of data mining combining with neural network classification methods to construct the best predictive model in dermatology. The results show that using neural network model has the highest, 92.62%, accuracy in prediction. Using sensitivity analysis combining with decision tree model, on the contrary, has the least accuracy, which is 80.33%. Based on this result, the AI classification technology can serve as important and useful references in diagnosis for physicians to avoid unnecessary medical waste and enhance health care quality.
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paper_title: A universal hybrid decision tree classifier design for human activity classification

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A system that reliably classifies daily life activities can contribute to more effective and economical treatments for patients with chronic conditions or undergoing rehabilitative therapy. We propose a universal hybrid decision tree classifier for this purpose. The tree classifier can flexibly implement different decision rules at its internal nodes, and can be adapted from a population-based model when supplemented by training data for individuals. The system was tested using seven subjects each monitored by 14 triaxial accelerometers. Each subject performed fourteen different activities typical of daily life. Using leave-one-out cross validation, our decision tree produced average classification accuracies of 89.9%. In contrast, the MATLAB personalized tree classifiers using Gini's diversity index as the split criterion followed by optimally tuning the thresholds for each subject yielded 69.2%.
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paper_title: Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules

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In this paper, we develop a diagnosis model based on particle swarm optimization (PSO), support vector machines (SVMs) and association rules (ARs) to diagnose erythemato-squamous diseases. The proposed model consists of two stages: first, AR is used to select the optimal feature subset from the original feature set; then a PSO based approach for parameter determination of SVM is developed to find the best parameters of kernel function (based on the fact that kernel parameter setting in the SVM training procedure significantly influences the classification accuracy, and PSO is a promising tool for global searching). Experimental results show that the proposed AR_PSO-SVM model achieves 98.91% classification accuracy using 24 features of the erythemato-squamous diseases dataset taken from UCI (University of California at Irvine) machine learning database. Therefore, we can conclude that our proposed method is very promising compared to the previously reported results.
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paper_title: An introduction to Support Vector Machines

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This book is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. The book also introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc. Their first introduction in the early 1990s lead to a recent explosion of applications and deepening theoretical analysis, that has now established Support Vector Machines along with neural networks as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and application of these techniques. The concepts are introduced gradually in accessible and self-contained stages, though in each stage the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally the book will equip the practitioner to apply the techniques and an associated web site will provide pointers to updated literature, new applications, and on-line software.
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paper_title: Diagnostic study on arrhythmia cordis based on particle swarm optimization-based support vector machine

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Diagnosis of arrhythmia cordis is very significant to ensure human health and save human lives. Support vector machine (SVM) is a new machine learning method based on statistical learning theory, which can solve the classification problem with small sampling, non-linear and high dimension. However, the practicability of SVM is affected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behavior of bird flocking or fish schooling. The optimization method not only has strong global search capability, but also is very easy to implement. Thus, in the study, the proposed PSO-SVM model is applied to diagnosis of arrhythmia cordis, in which PSO is used to determine free parameters of support vector machine. The experimental data from MIT-BIH ECG database are used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve higher diagnostic accuracy than artificial neural network in diagnosis of arrhythmia cordis.
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paper_title: Prediction model building and feature selection with support vector machines in breast cancer diagnosis

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Breast cancer is a serious problem for the young women of Taiwan. Some medical researches have proved that DNA viruses are one of the high-risk factors closely related to human cancers. Five DNA viruses are studied in this research: specific types of HSV-1 (herpes simplex virus type 1), EBV (Epstein-Barr virus), CMV (cytomegalovirus), HPV (human papillomavirus), and HHV-8 (human herpesvirus-8). The purposes of this study are to obtain the bioinformatics about breast tumor and DNA viruses, and to build an accurate diagnosis model about breast cancer and fibroadenoma. Research efforts have reported with increasing confirmation that the support vector machine (SVM) has a greater accurate diagnosis ability. Therefore, this study constructs a hybrid SVM-based strategy with feature selection to render a diagnosis between the breast cancer and fibroadenoma and to find the important risk factor for breast cancer. The results show that {HSV-1, HHV-8} or {HSV-1, HHV-8, CMV} are the most important features and that the diagnosis model achieved high classification accuracy, at 86% of average overall hit rate. A Linear discriminate analysis (LDA) diagnosis model is also constructed in this study. The LDA model shows that {HSV-1, HHV-8, EBV} or {HSV-1, HHV-8} are significant factors which are similar to that of the SVM-based classifier. However, the classificatory accuracy of the SVM-based classifier is slightly better than that of LDA in the negative hit ratio, positive hit ratio, and overall hit ratio.
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paper_title: The Support Vector Method of Function Estimation

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This chapter describes the Support Vector technique for function estimation problems such as pattern recognition, regression estimation, and solving linear operator equations. It shows that for the Support Vector method both the quality of solution and the complexity of the solution does not depend directly on the dimensionality of an input space. Therefore, on the basis of this technique one can obtain a good estimate using a given number of high-dimensional data.
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paper_title: Statistical learning theory

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A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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paper_title: A gene selection approach for classifying diseases based on microarray datasets

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Gene Selection is very important problem in the classification of serious diseases in clinical information systems. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analysis. In the current work, a hybrid approach is presented in order to classify diseases, such as colon cancer, leukemia, and liver cancer, based on informative genes. This hybrid approach uses clustering (K-means) with statistical analysis (ANOVA) as a preprocessing step for gene selection and Support Vector Machines (SVM) to classify diseases related to microarray experiments. To compare the performance of the proposed methodology, two kinds of comparisons were achieved: 1) applying statistical analysis combined with clustering algorithm (K-means) as a preprocessing step and 2) comparing different classification algorithms: decision tree (ID3), naive bayes, adaptive naive bayes, and support vector machines. In case of combining clustering with statistical analysis, much better classification accuracy is given of 97% rather than without applying clustering in the preprocessing phase. In addition, SVM had proven better accuracy than decision trees, Naive Bayes, and Adaptive Naive Bayes classification.
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paper_title: A new intelligent diagnosis system for the heart valve diseases by using genetic-SVM classifier

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In this study, an intelligent system based on genetic-support vector machines (GSVM) approach is presented for classification of the Doppler signals of the heart valve diseases. This intelligent system deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler ultrasound. GSVM is used in this study for diagnosis of the heart valve diseases. The GSVM selects of most appropriate wavelet filter type for problem, wavelet entropy parameter, the optimal kernel function type, kernel function parameter, and soft margin constant C penalty parameter of support vector machines (SVM) classifier. The performance of the GSVM system proposed in this study is evaluated in 215 samples. The test results show that this GSVM system is effective to detect Doppler heart sounds. The averaged rate of correct classification rate was about 95%.
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paper_title: Chest diseases diagnosis using artificial neural networks

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Chronic obstructive pulmonary, pneumonia, asthma, tuberculosis, lung cancer diseases are the most important chest diseases. These chest diseases are important health problems in the world. In this study, a comparative chest diseases diagnosis was realized by using multilayer, probabilistic, learning vector quantization, and generalized regression neural networks. The chest diseases dataset were prepared by using patient's epicrisis reports from a chest diseases hospital's database.
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paper_title: Data Mining: Introductory and Advanced Topics

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I. INTRODUCTION. 1. Introduction. 2. Related Concepts. 3. Data Mining Techniques. II. CORE TOPICS. 4. Classification. 5. Clustering. 6. Association Rules. III. ADVANCED TOPICS. 7. Web Mining. 8. Spatial Mining. 9. Temporal Mining. IV. APPENDIX. 10. Data Mining Products.
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paper_title: Bayesian network model for diagnosis of psychiatric diseases

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Because of numerous possible causes involved, it isn't easy for general physicians to identify the precise reason of the psychiatric diseases and to decide the correct treatment. Bayesian networks are recognized as efficient graphical models with significant capabilities for investigating biomedical data either to obtain relationships between variables, either for medical predictions. Our paper provides a Bayesian network-based analysis of psychiatric patient data, which have been gathered from a Romanian specialized clinic during a couple of years. The development of this Bayesian network led us to the identification of most significant factors that affect some important diseases and their correlations.
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paper_title: BBN-Based Decision Support for Health Risk Analysis

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Human health risk assessment (HRA) is the process to estimate the nature and probability of adverse health effects in humans. There are three issues about HRA: (1) hazard index (HI) cannot detail related diseases; moreover, we cannot exactly recognize the meaning of HI levels; (2) the same problems will also occur in human cancer risk; (3) the assumption of additive effect in human health for more than two toxic chemicals is problematic due to the violation of toxicology. To address these issues, this study proposes to use Bayesian belief networks (BBN) as a decision support for the higher level risk estimate which is able to represent the probabilistic relationships between all kinds of health effects and air pollutants. The study has several objectives: (1) to design the health risk procedure for BBN approach; (2) to represent the relationships between toxic substances, diseases and human effects with BBN; (3) to construct dose-response relationships by BBN; (4) to predict human disease and cancer risks due to specific toxic substance by BBN; and (5) case studies.
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paper_title: Weighted support vector regression approach for remote healthcare monitoring

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With ever increasing growth in science and technology, quality of life is improving day by day and health becomes a major concern for everyone. Since many people living in nuclear families, special measures need to be taken especially for the old age people living all alone. Wireless sensor network has become popular due to advancement in the communication capabilities of data, computational processing power and low power microelectronic devices and micro sensors. Body Area sensor network is a collection of wearable sensor nodes that implanted on human body for providing continuous monitoring of health conditions. Basically, we are focusing on how to predict the activity based on sensor reading. This research paper predicts the activity that is based on sensor reading so that we can provide continuous monitoring to patients in order to provide pervasive healthcare. It is in contrast with the traditional event driven approach where a patient visits the doctor only when he is sick. Prediction of activity is done using Support vector Regression (SVR). This paper presents a model to overcome the over-fitting which is due to noise and outliers in dataset. So, we propose an approach which uses weight factor to reduce the prediction error and result in higher accuracy than simple support vector regression.
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paper_title: Linking Empirical Estimates of Body Burden of Environmental Chemicals and Wellness using NHANES Data

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Biomonitoring of industrial chemicals in human tissues and fluids has shown that all people carry a “body burden” of synthetic chemicals. Although measurement of an environmental chemical in a person’s tissues/fluids is an indication of exposure, it does not necessarily mean the exposure concentration is sufficient to cause an adverse effect. Since humans are exposed to multiple chemicals, there may be a combination effect (e.g., additive, synergistic) associated with low-level exposures to multiple classes of contaminants, which may impact a variety of organ systems. The objective of this research is to link measures of body burden of environmental chemicals and a “holistic” measure of wellness. The approach is demonstrated using biomonitoring data from the National Health and Nutrition Examination Surveys (NHANES). Forty-two chemicals were selected for analysis based on their detection levels. Six biological pathway-specific indices were evaluated using groups of chemicals associated with each pathway. Five of the six pathways were negatively associated with wellness. Three non-zero interaction terms were detected which may provide empirical evidence of crosstalk across pathways. The approach identified five of the 42 chemicals from a variety of classes (metals, pesticides, furans, polycyclic aromatic hydrocarbons) as accounting for 71% of the weight linking body burden to wellness. Significant interactions were detected indicating the effect of smoking is exacerbated by body burden of environmental chemicals. Use of a holistic index on both sides of the exposure-health equation is a novel and promising empirical “systems biology” approach to risk evaluation of complex environmental exposures.
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paper_title: Logistic Regression by Means of Evolutionary Radial Basis Function Neural Networks

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This paper proposes a hybrid multilogistic methodology, named logistic regression using initial and radial basis function (RBF) covariates. The process for obtaining the coefficients is carried out in three steps. First, an evolutionary programming (EP) algorithm is applied, in order to produce an RBF neural network (RBFNN) with a reduced number of RBF transformations and the simplest structure possible. Then, the initial attribute space (or, as commonly known as in logistic regression literature, the covariate space) is transformed by adding the nonlinear transformations of the input variables given by the RBFs of the best individual in the final generation. Finally, a maximum likelihood optimization method determines the coefficients associated with a multilogistic regression model built in this augmented covariate space. In this final step, two different multilogistic regression algorithms are applied: one considers all initial and RBF covariates (multilogistic initial-RBF regression) and the other one incrementally constructs the model and applies cross validation, resulting in an automatic covariate selection [simplelogistic initial-RBF regression (SLIRBF)]. Both methods include a regularization parameter, which has been also optimized. The methodology proposed is tested using 18 benchmark classification problems from well-known machine learning problems and two real agronomical problems. The results are compared with the corresponding multilogistic regression methods applied to the initial covariate space, to the RBFNNs obtained by the EP algorithm, and to other probabilistic classifiers, including different RBFNN design methods [e.g., relaxed variable kernel density estimation, support vector machines, a sparse classifier (sparse multinomial logistic regression)] and a procedure similar to SLIRBF but using product unit basis functions. The SLIRBF models are found to be competitive when compared with the corresponding multilogistic regression methods and the RBFEP method. A measure of statistical significance is used, which indicates that SLIRBF reaches the state of the art.
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paper_title: Applied regression analysis, linear models, and related methods

paper_content:

PART ONE: PRELIMINARIES Statistics and Social Science What Is Regression Analysis? Examining Data Transforming Data PART TWO: LINEAR MODELS AND LEAST SQUARES Linear Least-Squares Regression Statistical Inference for Regression Dummy-Variable Regression Analysis of Variance Statistical Theory for Linear Models The Vector Geometry of Linear Models PART THREE: LINEAR-MODEL DIAGNOSTICS Unusual and Influential Data Diagnosing Nonlinearity, Nonconstant Error Variance, and Nonnormality Collinearity and Its Purported Remedies PART FOUR: BEYOND LINEAR LEAST SQUARES Extending Linear Least Squares Time Series, Nonlinear, Robust, and Nonparametric Regression Logit and Probit Models Assessing Sampling Variation Bootstrapping and Cross-Validation
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paper_title: A Clustering Genetic Algorithm for Genomic Data Mining

paper_content:

In this chapter we summarize our work toward developing clustering algorithms based on evolutionary computing and its application to genomic data mining. We have focused on the reconstruction of protein-protein functional interactions from genomic data. The discovery of functional modules of proteins is formulated as an optimization problem in which proteins with similar genomic attributes are grouped together. By considering gene co-occurrence, gene directionality and gene proximity, clustering genetic algorithms can predict functional associations accurately. Moreover, clustering genetic algorithms eliminate the need for the a priori specification of clustering parameters (e. g. number of clusters, initial position of centroids, etc.). Several methods for the reconstruction of protein interactions are described, including single-objective and multi-objective clustering genetic algorithms. We present our preliminary results on the reconstruction of bacterial operons and protein associations as specified by the DIP and ECOCYC databases.
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paper_title: Data Mining Concepts and Techniques

paper_content:

Understand the need for analyses of large, complex, information-rich data sets. Identify the goals and primary tasks of the data-mining process. Describe the roots of data-mining technology. Recognize the iterative character of a data-mining process and specify its basic steps. Explain the influence of data quality on a data-mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers. In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining, which produces new, nontrivial information based on the available data set.
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paper_title: Clustering in the Service of the Public's Health

paper_content:

1. Abstract Our purpose here is to provide an overview of some recent developments in cluster analysis and their applications to health services research. To illustrate, we show how k-means clustering can be used to partition a populaton of patients with depression into clinically relevant health states and to analyze how they change over time. Among the issues discussed are preprocessing of data, identifying the "right" number of clusters, implementation via the Lloyd algorithm, reverse engineering of health state descriptions from clusters, and longitudinal modeling of patients’ transitions.
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paper_title: Learning the k in k-means

paper_content:

When clustering a dataset, the right number k of clusters to use is often not obvious, and choosing k automatically is a hard algorithmic problem. In this paper we present an improved algorithm for learning k while clustering. The G-means algorithm is based on a statistical test for the hypothesis that a subset of data follows a Gaussian distribution. G-means runs k-means with increasing k in a hierarchical fashion until the test accepts the hypothesis that the data assigned to each k-means center are Gaussian. Two key advantages are that the hypothesis test does not limit the covariance of the data and does not compute a full covariance matrix. Additionally, G-means only requires one intuitive parameter, the standard statistical significance level α. We present results from experiments showing that the algorithm works well, and better than a recent method based on the BIC penalty for model complexity. In these experiments, we show that the BIC is ineffective as a scoring function, since it does not penalize strongly enough the model's complexity.
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paper_title: Clustering-based approach for detecting breast cancer recurrence

paper_content:

This paper aims to assess the effectiveness of three different clustering algorithms, used to detect breast cancer recurrent events. The performance of a classical k-means algorithm is compared with a much more sophisticated Self-Organizing Map (SOM-Kohonen network) and a cluster network, closely related to both k-means and SOM. The three clustering algorithms have been applied on a concrete breast cancer dataset, and the result clearly showed that the best performance was obtained by the cluster network, followed by SOM and k-means, their predicting accuracy ranging from 62% to 78%. Based on the patients' segmentation regarding the occurrence of recurrent events, new patients may be labeled according to their medical characteristics as developing or not recurrent events, thus supporting health professionals in making informed decisions.
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paper_title: Early detection and characterization of Alzheimer's disease in clinical scenarios using Bioprofile concepts and K-means

paper_content:

Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. There is a need for objective means to detect AD early to allow targeted interventions and to monitor response to treatment. To help clinicians in these tasks, we propose the creation of the Bioprofile of AD. A Bioprofile should reveal key patterns of a disease in the subject's biodata. We applied k-means clustering to data features taken from the ADNI database to divide the subjects into pathologic and non-pathologic groups in five clinical scenarios. The preliminary results confirm previous findings and show that there is an important AD pattern in the biodata of controls, AD, and Mild Cognitive Impairment (MCI) patients. Furthermore, the Bioprofile could help in the early detection of AD at the MCI stage since it divided the MCI subjects into groups with different rates of conversion to AD.
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paper_title: An analysis on the impact of fluoride in human health (dental) using clustering data mining technique

paper_content:

Data Mining is the process of extracting information from large data sets through using algorithms and Techniques drawn from the field of Statistics, Machine Learning and Data Base Management Systems. Traditional data analysis methods often involve manual work and interpretation of data which is slow, expensive and highly subjective Data Mining, popularly called as knowledge discovery in large data, enables firms and organizations to make calculated decisions by assembling, accumulating, analyzing and accessing corporate data. It uses variety of tools like query and reporting tools, analytical processing tools, and Decision Support System. [1][2] This article explores data mining techniques in health care. In particular, it discusses data mining and its application in areas where people are affected severely by using the under-ground drinking water which consist of high levels of fluoride in Krishnagiri District, Tamil Nadu State, India. This paper identifies the risk factors associated with the high level of fluoride content in water, using clustering algorithms and finds meaningful hidden patterns which give meaningful decision making to this socio-economic real world health hazard.
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paper_title: Data clustering: a review

paper_content:

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overviewof pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
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paper_title: Data Mining Concepts and Techniques

paper_content:

Understand the need for analyses of large, complex, information-rich data sets. Identify the goals and primary tasks of the data-mining process. Describe the roots of data-mining technology. Recognize the iterative character of a data-mining process and specify its basic steps. Explain the influence of data quality on a data-mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers. In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining, which produces new, nontrivial information based on the available data set.
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paper_title: A combined K-means and hierarchical clustering method for improving the clustering efficiency of microarray

paper_content:

Among the microarray data analysis clustering methods, K-means and hierarchical clustering are researchers' favorable tools today. However, each of these traditional clustering methods has its limitations. In this study, we introduce a new method, hierarchical K-means regulating divisive or agglomerative approach. The hierarchical K-means firstly employs K-means' algorithm in each cluster to determine K cluster while operating and then employs it on hierarchical clustering technique to shorten merging clusters time while generating a tree-like dendrogram. We apply this method in two original microarray datasets. The result indicates divisive hierarchical K-means is superior to hierarchical clustering on cluster quality and is superior to K-means clustering on computational speed. Our conclusion is that divisive hierarchical K-means establishes a better clustering algorithm satisfying researchers' demand.
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paper_title: Hybrid hierarchical clustering with applications to microarray data

paper_content:

SUMMARY In this paper, we propose a hybrid clustering method that combines the strengths of bottom-up hierarchical clustering with that of top-down clustering. The first method is good at identifying small clusters but not large ones; the strengths are reversed for the second method. The hybrid method is built on the new idea of a mutual cluster: a group of points closer to each other than to any other points. Theoretical connections between mutual clusters and bottom-up clustering methods are established, aiding in their interpretation and providing an algorithm for identification of mutual clusters. We illustrate the technique on simulated and real microarray datasets.
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paper_title: Prediction of Disease Severity in Patients with Early Rheumatoid Arthritis by Gene Expression Profiling

paper_content:

In order to test the ability of peripheral blood gene expression profiles to predict future disease severity in patients with early rheumatoid arthritis (RA), a group of 17 patients (1 ± 0.2 years disease duration) was evaluated at baseline for gene expression profiles. Disease status was evaluated after a mean of 5 years using an index combining pain, global and recoded MHAQ scores. Unsupervised and supervised algorithms identified “predictor genes” whose combined expression levels correlated with follow-up disease severity scores. Unsupervised clustering algorithms separated patients into two branches. The only significant difference between these two groups was the disease severity score; demographic variables and medication usage were not different. Supervised T-Test analysis identified 19 “predictor genes” of future disease severity. Results were validated in an independent cohort of subjects of established RA with using Support Vector Machines and K-Nearest-Neighbor Classification. Our study demonstrates that peripheral blood gene expression profiles may be a useful tool to predict future disease severity in patients with early and established RA.
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paper_title: Data Mining Concepts and Techniques

paper_content:

Understand the need for analyses of large, complex, information-rich data sets. Identify the goals and primary tasks of the data-mining process. Describe the roots of data-mining technology. Recognize the iterative character of a data-mining process and specify its basic steps. Explain the influence of data quality on a data-mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers. In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining, which produces new, nontrivial information based on the available data set.
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paper_title: Mining biomedical images with density-based clustering

paper_content:

Density-based clustering algorithms have recently gained popularity in the data mining field due to their ability to discover arbitrary shaped clusters while preserving spatial proximity of data points. In this work we adapt a density-based clustering algorithm, DBSCAN, to a new problem domain: identification of homogenous color regions in biomedical images. Examples of specific problems of this nature include landscape segmentation of satellite imagery, object detection and, in our case, identification of significant color regions in images of skin lesions (tumors). Automated outer and inner boundary segmentation is a key step in segmentation of structures such as skin lesions, tumors of breast, bone, and brain. This step is important because the accuracy of the subsequent steps (extraction of various features, post-processing) crucially depends on the accuracy of this very first step. In this paper, we present an unsupervised approach to segmentation of pigmented skin lesion images based on DBSCAN clustering algorithm. The color regions identified by the algorithm are compared to those identified by the human subjects and the Kappa coefficient, a statistical indicator of computer-human agreement, is found to be significant.
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paper_title: Predictive models for dengue outbreak using multiple rulebase classifiers

paper_content:

The paper aims to develop the predictive models for dengue outbreak detection using Multiple Rule Based Classifiers. The rule based classifiers used are the Decision Tree, Rough Set Classifier, Naive Bayes, and Associative Classifier. Dengue fever (DF) and dengue hemorrhagic fever (DHF) have been continuously becoming a public health related issues in Malaysia and growing pandemic as reported by World Health Organization (WHO). It is important for the government to able to make early detection for dengue outbreak. Thus, to improve early detection of the dengue outbreak and making such strategic planning and decision, being able to predict or forecast the possible dengue outbreak in an area is critically important. The purpose of the classification modelling is to build a predictive model for predicting the dengue outbreak. Since to date there is no research uses this data for predictive modelling, several classifiers are investigated to study the performance of various rule based classifiers individually and the combination of the classifiers. The experimental results show that the multiple classifiers are able produce better accuracy (up to 70%) with more quality rules compared to the single classifier.
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paper_title: Mining association rules between sets of items in large databases

paper_content:

We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
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paper_title: Mining Infrequent Causal Associations in Electronic Health Databases

paper_content:

Discovering infrequent causal relationships can help us prevent or correct negative outcomes caused by their antecedents. In this paper, we propose an innovative data mining framework and apply it to mine potential causal associations in electronic patient datasets where the drug-related events of interest occur infrequently. Specifically, we created a novel interestingness measure, exclusive causal-leverage, based on a computational, fuzzy recognition-primed decision (RPD) model that we previously developed. On the basis of this new measure, a data mining algorithm was developed to mine the causal relationship between drugs and their associated adverse drug reactions (ADRs). The algorithm was tested on real patient data retrieved from the Veterans Affairs Medical Center in Detroit, Michigan. The exclusive causal-leverage was employed to rank the potential causal associations between each of the two selected drugs (i.e., enalapril and pravastatin) and 3,954 recorded symptoms, each of which corresponds to a potential ADR. The top 10 drug-symptom pairs for each drug were evaluated by our physicians on the project team. The results showed that the number of symptoms considered as real ADRs for enalapril and pravastatin was 8 and 7 out of 10, respectively.
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paper_title: Association Rule for Classification of Type-2 Diabetic Patients

paper_content:

The discovery of knowledge from medical databases is important in order to make effective medical diagnosis. The aim of data mining is extract the information from database and generate clear and understandable description of patterns. In this study we have introduced a new approach to generate association rules on numeric data. We propose a modified equal width binning interval approach to discretizing continuous valued attributes. The approximate width of the desired intervals is chosen based on the opinion of medical expert and is provided as an input parameter to the model. First we have converted numeric attributes into categorical form based on above techniques. Apriori algorithm is usually used for the market basket analysis was used to generate rules on Pima Indian diabetes data. The data set was taken from UCI machine learning repository containing total instances 768 and 8 numeric attributes.We discover that the often neglected pre-processing steps in knowledge discovery are the most critical elements in determining the success of a data mining application. Lastly we have generated the association rules which are useful to identify general associations in the data, to understand the relationship between the measured fields whether the patient goes on to develop diabetes or not. We are presented step-by-step approach to help the health doctors to explore their data and to understand the discovered rules better.
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paper_title: Mining medical data to identify frequent diseases using Apriori algorithm

paper_content:

The data mining is a process of analyzing a huge data from different perspectives and summarizing it into useful information. The information can be converted into knowledge about historical patterns and future trends. Data mining plays a significant role in the field of information technology. Health care industry today generates large amounts of complex data about patients, hospitals resources, diseases, diagnosis methods, electronic patients records, etc,. The data mining techniques are very useful to make medicinal decisions in curing diseases. The healthcare industry collects huge amount of healthcare data which, unfortunately, are not “mined” to discover hidden information for effective decision making. The discovered knowledge can be used by the healthcare administrators to improve the quality of service. In this paper, authors developed a method to identify frequency of diseases in particular geographical area at given time period with the aid of association rule based Apriori data mining technique.
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paper_title: Analysis of effectiveness of apriori algorithm in medical billing data mining

paper_content:

Apriori algorithm has been successfully used for finding the frequent item sets in retail data. This research is about finding associations between diagnosis and treatments. It has shown that the algorithm is equally beneficent for finding the large item sets and thus generating the association rules in medical billing data. Resemblance between medical bill and purchase bill is the motivation of using Apriori algorithm in this research work. Healthcare is a data rich domain. Medical practices, insurance companies and other health related organizations have collected huge volumes of data, thus attracting data mining researchers to explore it and find something beneficent from it.
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paper_title: Association rule mining to detect factors which contribute to heart disease in males and females

paper_content:

This paper investigates the sick and healthy factors which contribute to heart disease for males and females. Association rule mining, a computational intelligence approach, is used to identify these factors and the UCI Cleveland dataset, a biological database, is considered along with the three rule generation algorithms - Apriori, Predictive Apriori and Tertius. Analyzing the information available on sick and healthy individuals and taking confidence as an indicator, females are seen to have less chance of coronary heart disease then males. Also, the attributes indicating healthy and sick conditions were identified. It is seen that factors such as chest pain being asymptomatic and the presence of exercise-induced angina indicate the likely existence of heart disease for both men and women. However, resting ECG being either normal or hyper and slope being flat are potential high risk factors for women only. For men, on the other hand, only a single rule expressing resting ECG being hyper was shown to be a significant factor. This means, for women, resting ECG status is a key distinct factor for heart disease prediction. Comparing the healthy status of men and women, slope being up, number of coloured vessels being zero, and oldpeak being less than or equal to 0.56 indicate a healthy status for both genders.
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paper_title: Discovering pattern in medical audiology data with FP-growth algorithm

paper_content:

There is potential knowledge inherent in vast amounts of untapped and possibly valuable data generated by healthcare providers. So often, clinicians rely in their skills and experience and that of other medical experts as their source of information. The healthcare sector is now capturing more data in the form of digital and non digital ::: format that may potentially be mined to generate valuable ::: insights. In this paper we propose a five step knowledge ::: discovery model to discover patterns in medical audiology ::: records. We use frequent pattern growth (FP-Growth) ::: algorithm in the data processing step to build the FP-tree ::: data structure and mine it for frequents itemsets. Our aim is to discover interesting itemsets that shows connection ::: between hearing thresholds in pure-tone audiometric data ::: and symptoms from diagnosis and other attributes in the ::: medical records. The experimental results are summaries of ::: frequent structures in the data that contains symptoms of ::: tinnitus, vertigo and giddiness with threshold values and ::: other information like gender.
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paper_title: Specialised Tools for Automating Data Mining for Hospital Management

paper_content:

This paper presents a research project which is directed to the partial automation of Data Mining (DM) in hospital information systems (HIS). We concentrate on hospital management applications and information systems such as emergencies and ward management, human resources (services, night duties, etc.), physical resources (beds, intervention theatres, etc.), etc. We have realised how the business objectives are usually the same across several hospitals and so is the information which is gathered in several HIS (even using different DBMS). This means that although the models extracted highly differ between hospitals, data mining processes are highly similar across different hospitals. We argue how a tool can be constructed in such a way that it automates many DM processes and that can be ported to other hospitals which could benefit more quickly of a first DM experience. Our work plan covers all the stages in the process of Knowledge Discovery from Databases (KDD): data cleansing, extraction and integration from the HIS and external data, construction of tasks and minable views, model generation, and finally a module to carry out and interpret their predictions. We also consider a module to perform simulations and to integrate the models extracted by the previous modules with other decision support systems as well as model monitoring.
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Title: A Survey on Data Mining Approaches for Healthcare

Section 1: Introduction
Description 1: Provide an overview of how data mining is applied in the healthcare field, its importance, and the various benefits it offers.

Section 2: Data Mining
Description 2: Discuss the origins, processes, and significance of data mining, and its specific application in the healthcare sector.

Section 3: Decision Tree (DT)
Description 3: Explain the decision tree methodology, its construction, and its applications in healthcare for classification problems.

Section 4: Support Vector Machine (SVM)
Description 4: Describe the concept and working of SVM, particularly focusing on its use for classification in healthcare.

Section 5: Neural Network (NN)
Description 5: Detail the neural network algorithm and its utilization for classification and pattern recognition in medical data.

Section 6: Bayesian Methods
Description 6: Outline Bayesian classification methods, including Naive Bayes and Bayesian Belief Networks, and their applications in healthcare.

Section 7: Regression
Description 7: Provide a comprehensive explanation of regression techniques, including linear and logistic regression, and their relevance in predicting healthcare outcomes.

Section 8: Clustering
Description 8: Discuss clustering methods and their role in handling and analyzing large healthcare datasets.

Section 9: Partitioned Clustering
Description 9: Elaborate on partitioned clustering techniques like K-means and K-medoids, and their specific applications in the healthcare domain.

Section 10: Hierarchical Clustering
Description 10: Explain hierarchical clustering methods, including agglomerative and divisive approaches, and their use cases in healthcare.

Section 11: Density Based Clustering
Description 11: Delve into density-based clustering methods such as DBSCAN and their application in handling complex health data.

Section 12: Association
Description 12: Discuss the association technique, including Apriori and frequent pattern tree algorithms, and their use in discovering relationships in healthcare data.

Section 13: Application of Data Mining in Health
Description 13: Provide examples of how data mining is applied in various aspects of healthcare like hospital resource management, patient care, and fraud detection.

Section 14: Data Mining Challenges in Healthcare
Description 14: Highlight the significant challenges faced in data mining within healthcare, including data quality, privacy, and the complexity of medical data.

Section 15: Conclusion and Future Issues
Description 15: Summarize the key points discussed in the paper and provide insights into the future direction and potential improvements in data mining for healthcare.