title: Application of fuzzy-classifier system to coronary artery disease and breast cancer.
abstract: This paper presents an application of a genetic-algorithm-based representation of fuzzy rules for the classification of coronary artery disease data and breast cancer data. The performance of this fuzzy classifier for classification of coronary artery disease and breast cancer data is evaluated. In this study the concept of fuzzy if-then has been applied of rules proposed by Ishibuchi et al. for a multi dimensional data classification problem which leads to higher classification power. The fitness value of each fuzzy if-then rule was determined by the numbers of correctly and wrongly classified training patterns for that rule. The classification power on real world data for coronary artery disease and breast cancer was thus demonstrated by computer simulations.
authors.affiliation: Department of Applied Mathematics, University of Adelaide, South Australia.
authors.forename: R
authors.initials: R
authors.lastname: Jain
authors.forename: J
authors.initials: J
authors.lastname: Mazumdar
authors.forename: W
authors.initials: W
authors.lastname: Moran
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis*; D003198:Computer Simulation; D003327:Coronary Disease / Q000175:diagnosis*; D003627:Data Interpretation, Statistical; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer