title: Weighted fuzzy classification with integrated learning method for medical diagnosis.
abstract: Medical diagnosis can be viewed as a pattern classification problem: based a set of input features the goal is to classify a patient as having a particular disorder or as not having it. Performance of medical diagnosis is typically assessed in terms of sensitivity and specificity. In this paper we introduce a pattern classification system for medical diagnosis that is based on fuzzy logic and utilises weighted training patterns. Adjusting the weights allows to focus either on sensitivity or specificity while not neglecting the other one and hence lends a degree of flexibility to the diagnostic process. A learning method is utilised that provides improved classification performance. Excellent classification results based on the University of Wisconsin breast cancer database are presented.
authors.affiliation: College of Engineering, Osaka Prefecture University, Osaka, Japan, nakashi@cs.osakafu-u.ac.jp.
authors.forename: Tomoharu
authors.initials: T
authors.lastname: Nakashima
authors.forename: Gerald
authors.initials: G
authors.lastname: Schaefer
authors.forename: Yasuyuki
authors.initials: Y
authors.lastname: Yokota
authors.forename: Shao
authors.initials: S
authors.lastname: Ying Zhu
authors.forename: Hisao
authors.initials: H
authors.lastname: Ishibuchi