A Fuzzy Expert System for Diagnosis of Epilepsy Diseases Using the Situational Logic and ACH Modeling in the Creation of Knowledge Base

Nowadays expert systems are used as one of the most useful and most practical decision support systems. These systems are relying on knowledge of experts in certain domain combines valuable experience with speed and accuracy of computer and improve the quality of their judgments. One of the most extensive applications of these systems is medical diagnostic fields. Different from what happened in most prior researches in the development of expert systems, in the present study using the situational logic in the process of knowledge acquisition and fuzzy inference engine architecture approach is recommended. Identify the type of epilepsy has always been one of the most challenging deliberations among the neurologist doctors and strict distinction between the types of the disease, according to the closely signs Creates conflict among the field's doctors that the expert system is able to solve this problem with the accuracy of 83 percent. Forming a comprehensive knowledge base using analysis of competing hypotheses (ACH) modeling in order to distinguish between 14 types of epilepsy disease is the distinctive features of this study. Research done on the project can be used to diagnose other diseases that have similar symptoms closely and to be pragmatic. The proposed system can be used in situations where access to neurologist doctors is impossible can be very useful.