Improving the fuzzy expert system for diagnosing depressive disorders

Abstract

This paper presents an improving knowledge base and inference engine of a medical expert system for diagnosing depressive disorders. This medical expert system calls PORUL.DEP. PORUL.DEP’s knowledge base includes more than 850 positive rules. PORUL.DEP has been tested on more than 260 medical records of depressed patients. PORUL.DEP gives a correct diagnosis of more than 95% with light depressive disorder and without depressive disorder, but the remaining depressive disorders are not accurate. Average percent of more than 24 %. A new expert system, called STRESSDIAG, was developed on combining positive rules (for confirmation of conclusion) and negative rules (for exclusion of conclusion) for diagnosing depressive disorders. STRESSDIAG’s knowledge base consists of more than 850 positive rules of PORUL.DEP and more than 120 negative rules. Abelian group operation of Mycin is used to improve the inference engine based on fuzzy relations. STRESSDIAG gives the correct diagnosis of more than 76% with 4 depressive disorders types and without depressive disorders. Average percent of more than 82 %, up nearly 60% compared to PORUL.DEP.