Enhancing Ayurvedic Diagnosis using Multinomial Naive Bayes and K-modes
  Clustering: An Investigation into Prakriti Types and Dosha Overlapping
Pranav Bidve Shalini Mishra Annapurna J
The identification of Prakriti types for the human body is a long-lost
medical practice in finding the harmony between the nature of human beings and
their behaviour. There are 3 fundamental Prakriti types of individuals. A
person can belong to any Dosha. In the existing models, researchers have made
use of SVM, KNN, PCA, Decision Tree, and various other algorithms. The output
of these algorithms was quite decent, but it can be enhanced with the help of
Multinomial Naive Bayes and K-modes clustering. Most of the researchers have
confined themselves to 3 basic classes. This might not be accurate in the
real-world scenario, where overlapping might occur. Considering these, we have
classified the Doshas into 7 categories, which includes overlapping of Doshas.
These are namely, VATT-Dosha, PITT-Dosha, KAPH-Dosha, VATT-PITT-Dosha,
PITT-KAPH-Dosha, KAPH-VATT-Dosha, and VATT-PITT-KAPH-Dosha. The data used
contains a balanced set of all individual entries on which preprocessing steps
of machine learning have been performed. Chi-Square test for handling
categorical data is being used for feature selection. For model fitting, the
method used in this approach is K-modes clustering. The empirical results
demonstrate a better result while using the MNB classifier. All key findings of
this work have achieved 0.90 accuracy, 0.81 precision, 0.91 F-score, and 0.90
recall. The discussion suggests a provident analysis of the seven clusters and
predicts their occurrence. The results have been consolidated to improve the
Ayurvedic advancements with machine learning.