A WEIGHTED FUZZY-REASONING ALGORITHM FOR MEDICAL DIAGNOSIS

This paper presents a weighted fuzzy reasoning algorithm for handling medical diagnostic problems, where fuzzy set theory and fuzzy production rules are used for knowledge representation. The algorithm can perform fuzzy matching between the patient's symptom manifestations and the antecedent portions of fuzzy production rules to determine the presence of diseases, where the result is interpreted as a certainty level indicating the degree of certainty of the presence of the disease. Because the algorithm allows each symptom in medical diagnosis to have a different degree of importance, it is more flexible than the ones we presented in [3] and [4]. The algorithm can be executed very efficiently. If the knowledge base contains n fuzzy production rules and there are p symptoms, then the time complexity of the algorithm is O(np).