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.
|Title of host publication||21st European Conference on Modelling and Simulation: Simulations in United Europe, ECMS 2007|
|Number of pages||6|
|Publication status||Published - 1 Dec 2007|
|Event||21st European Conference on Modelling and Simulation, ECMS 2007 - Prague, United Kingdom|
Duration: 4 Jun 2007 → 6 Jun 2007
|Conference||21st European Conference on Modelling and Simulation, ECMS 2007|
|Period||4/06/07 → 6/06/07|