Performance evaluation of fuzzy rule-based systems with class priority for medical diagnosis problems

In this paper we examine the performance of fuzzy rule-based systems with classification priority for medical diagnosis problems. The assumption in this paper is that a classification priority is given a priori for each class in a pattern classification problem. Our fuzzy rule-based system consists of a set of fuzzy if-then rules that are automatically generated from a set of given training patterns. The consequent class of fuzzy if-then rules are decided based on the number of covered training patterns for each class. We apply the fuzzy classifier with class priority to two medical diagnosis problems: appendix diagnosis and breast cancer diagnosis, and compare its performance with that of a conventional fuzzy rule-based systems.