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Liver diseases pose a significant global health burden, impacting manyindividuals and having substantial economic and social consequences. Risingliver problems are considered a fatal disease in many countries, such as Egyptand Moldova. This study aims to develop a diagnosis and treatment model forliver disease using Basic Formal Ontology (BFO), Patient Clinical Data (PCD)ontology, and detection rules derived from a decision tree algorithm. For thedevelopment of the ontology, the National Viral Hepatitis Control Program(NVHCP) guidelines were used, which made the ontology more accurate andreliable. The Apache Jena framework uses batch processing to detect eventsbased on these rules. Based on the event detected, queries can be directlyprocessed using SPARQL. We convert these Decision Tree (DT) and medicalguidelines-based rules into Semantic Web Rule Language (SWRL) to operationalizethe ontology. Using this SWRL in the ontology to predict different types ofliver disease with the help of the Pellet and Drools inference engines inProtege Tools, a total of 615 records were taken from different liver diseases.After inferring the rules, the result can be generated for the patientaccording to the rules, and other patient-related details, along with differentprecautionary suggestions, can be obtained based on these results. These rulescan make suggestions more accurate with the help of Explainable ArtificialIntelligence (XAI) with open API-based suggestions. When the patient hasprescribed a medical test, the model accommodates this result using opticalcharacter recognition (OCR), and the same process applies when the patient hasprescribed a further medical suggestion according to the test report. Thesemodels combine to form a comprehensive Decision Support System (DSS) for thediagnosis of liver disease.

A Diagnosis and Treatment of Liver Diseases: Integrating Batch Processing, Rule-Based Event Detection and Explainable Artificial Intelligence