Adaptive Learning Expert System for Diagnosis and Management of Viral Hepatitis
This work addresses the diagnosis and management of viral hepatitis, a global health issue, but it appears incremental as it applies existing expert system techniques to this specific domain.
The paper tackles the problem of diagnosing and managing viral hepatitis by developing an adaptive learning expert system that acquires knowledge from internists and uses rule-based reasoning with forward and backward chaining to provide advice, resulting in a system that can adapt dynamically by generalizing and discovering new rules without needing a knowledge engineer.
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