Developing an Adaptive Learning Expert System for Diagnosis and Treatment of Alzheimer’s Disease

Abstract

Alzheimer’s is a disease of the brain that causes problems with memory, thinking and behaviour. It is not a normal part of aging. Alzheimer’s gets worse over time. Although symptoms can vary widely, the first problem many people notice is forgetfulness severe enough to affect their ability to function at home or at work, or to enjoy hobbies. The disease may cause a person to become confused, get lost in familiar places, misplace things or have trouble with language. However, among people in the developing countries like Nigeria, permanent diseases are growing to be causes of death. These problems are becoming worse due to the scarcity of specialists, practitioners and health facilities. In an effort to address such problem, this study attempts to design and develop a prototype adaptive learning expert system that can provide advice for physicians and patients to facilitate the diagnosis and treatment of Alzheimer’s disease patients. To this end, tacit knowledge is acquired from domain experts using interviewing technique and explicit knowledge is captured from medical documents through document analysis technique to find the solution of the problem. Then, the acquired knowledge is modelled using decision tree structure that represents concepts and procedures involved in diagnosis and treatment of Alzheimer’s disease and production rules are used to represent the domain knowledge and knowledge-based system is developed using SWI Prolog editor tool version 7.7.19. The system is tested and evaluated to ensure that whether the performance of the system is accurate and the system is usable by physicians and patients. The accuracy of the adaptive learning expert system is 79.5%. Thus, the prototype system achieves a good performance and meets the objectives of the study.