A CASE ASSESSMENT OF KNOWLEDGE-BASED FIT IN FRAME FOR DIAGNOSIS OF HUMAN EYE DISEASES

Abstract

The Eye is the most important sensory organ of vision function. But some eye diseases can lead to vision loss, so it is important to identify and treat eye disease as early as possible. Eye care professionals can help protect their patients from vision loss or blindness by recognizing common eye diseases and recommending for an eye exam. Eye diseases with early detection, treatment, and appropriate follow-up care, vision loss and blindness from eye disease can be prevented or delayed. In this study, rule-based eye disease identification and advising the knowledge-based system are projected. The projected system is targeting using hidden knowledge extracted by employing the extraction algorithm of data mining. To identify the best prediction model for the diagnosis of eye disease, four experiments for four classification algorithms were performed. Finally, the researchers decided to use the rules of the J48 pruned classification algorithm for further use in the development of a knowledge base of KBS because it exhibited better performance with a 98.5 % evaluation result. In this work, the integration is done between the J48 pruned classifier and PROLOG and converted from rule representation to PROLOG understandable format. Thus, SWI-Prolog 7.6.4 has used to implement the prototype of eye disease advising KBS and Java Net Beans IDE 8.2 with JDK 1.8.0 to integrate the model.