Adaptive Learning Expert System for Diagnosis and Management of Viral Hepatitis

TL;DR

This paper presents an adaptive expert system for diagnosing and managing viral hepatitis, capable of learning and updating its knowledge base dynamically from domain experts using rule-based reasoning.

Contribution

It introduces a novel adaptive learning mechanism enabling the expert system to generalize and discover new rules without external intervention.

Findings

System effectively diagnoses hepatitis types and stages

Adaptive learning improves knowledge base over time

Prototype developed using SWI-Prolog demonstrates practical applicability

Abstract

Viral hepatitis is the regularly found health problem throughout the world among other easily transmitted diseases, such as tuberculosis, human immune virus, malaria and so on. Among all hepatitis viruses, the uppermost numbers of deaths are result from the long-lasting hepatitis C infection or long-lasting hepatitis B. In order to develop this system, the knowledge is acquired using both structured and semi-structured interviews from internists of St.Paul Hospital. Once the knowledge is acquired, it is modeled and represented using rule based reasoning techniques. Both forward and backward chaining is used to infer the rules and provide appropriate advices in the developed expert system. For the purpose of developing the prototype expert system SWI-prolog editor also used. The proposed system has the ability to adapt with dynamic knowledge by generalizing rules and discover new rules through learning the newly arrived knowledge from domain experts adaptively without any help from the knowledge engineer. Keywords: Expert System, Diagnosis and Management of Viral Hepatitis, Adaptive Learning, Discovery and Generalization Mechanism