Integration of Rule-Based and Case-Based Reasoning for Diagnosis and Treatment of Eye Diseases

Abstract

The prevalence of eye disease is rapidly growing in Ethiopia and the scarcity of experts in the field of ophthalmology is the major problem for early diagnosis and treatment for eye patients. Designing KBS will simplify the medical diagnose processes by emulating human experts. In KBS development case-based and rule-based reasoning are popular techniques of knowledge representation for problem-solving and decision making. The main purpose of this research is to integrate rule-based and case-based reasoning approaches for diagnosing and treatment of eye diseases. The study followed the design science research approach with six steps process model. For problem identification and formulate the objective of the solution, knowledge is acquired by using document analysis, domain expert interviewing and previously solved patient cards form Jimma University Specialized Hospital. The method of integration of rule-base with case-based reasoning is done using a conditional combination model, which has a controller in between RBR and CBR. The controller is developed by Java eclipse programming language. It forwards the query first for rulebased reasoning (RBR) which attempts to recommend a solution for the new query. If RBR doesn‘t solve the problem, the query is automatically forwarded to the CBR system. The CBR system is developed by JCOLIBRI programming language, where the case retrieval module identifies the most related solution using case similarity measure. In this study, the four RE (Retrieval, Reuse, Revise and retain) cycle are integrated with the CBR system. The knowledge-based system prototype is demonstrated practically for users‘ acceptance evaluation. Experimental results show that the system achieves 87.5% accuracy with an average Precision and Recall of 94.7% and 90% respectively. For user acceptance testing the selected ten experts are involved for evaluation hereafter, the system scores 85.6%. This shows the system has registered a promising result. However, knowledge acquisition is performed manually which is challenging and incomplete. So, further study has to be done for automatic knowledge acquisition using data mining or machine learning for simplifying learning towards intelligent system design.