A Decision Support System for Liver Diseases Prediction: Integrating
  Batch Processing, Rule-Based Event Detection and SPARQL Query
Ritesh Chandra Sadhana Tiwari Satyam Rastogi Sonali Agarwal
Liver diseases pose a significant global health burden, impacting a
substantial number of individuals and exerting substantial economic and social
consequences. Rising liver problems are considered a fatal disease in many
countries, such as Egypt, Molda, etc. The objective of this study is to
construct a predictive model for liver illness using Basic Formal Ontology
(BFO) and detection rules derived from a decision tree algorithm. Based on
these rules, events are detected through batch processing using the Apache Jena
framework. Based on the event detected, queries can be directly processed using
SPARQL. To make the ontology operational, these Decision Tree (DT) rules are
converted into Semantic Web Rule Language (SWRL). Using this SWRL in the
ontology for predicting different types of liver disease with the help of the
Pellet and Drool inference engines in Protege Tools, a total of 615 records are
taken from different liver diseases. After inferring the rules, the result can
be generated for the patient according to the DT rules, and other
patient-related details along with different precautionary suggestions can be
obtained based on these results. Combining query results of batch processing
and ontology-generated results can give more accurate suggestions for disease
prevention and detection. This work aims to provide a comprehensive approach
that is applicable for liver disease prediction, rich knowledge graph
representation, and smart querying capabilities. The results show that
combining RDF data, SWRL rules, and SPARQL queries for analysing and predicting
liver disease can help medical professionals to learn more about liver diseases
and make a Decision Support System (DSS) for health care.