Cost-sensitive fuzzy classification for medical diagnosis

G. Schaefer, T. Nakashima, Y. Yokota, H. Ishibuchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Medical diagnosis essentially represents a pattern classification problem: based on a certain input an expert arrives at a diagnosis which often takes on a binary form, i.e. The patient suffering from a certain disease or not. A lot of research has focussed on computer assisted diagnosis where objective measurements are passed to a classifier algorithm which then proposes diagnostic output based on a previous learning process. However, these classifiers put equal emphasis on a learning patterns irrespective of the class they belong to. In this paper we apply a fuzzy rule-based classification system to medical diagnosis. Importantly, we extend the classifier to incorporate a concept of cost which can be used to emphasize those cases that signify illness as it is usually more costly to incorrectly diagnose such a patient as being healthy. Experimental results on various medical datasets confirm the usefulness and efficacy of our approach.

Original languageEnglish
Title of host publication2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007
Pages312-316
Number of pages5
Publication statusPublished - 1 Dec 2007
Event2007 4th IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2007 - Honolulu, HI, United Kingdom
Duration: 1 Apr 20075 Apr 2007

Conference

Conference2007 4th IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2007
CountryUnited Kingdom
CityHonolulu, HI
Period1/04/075/04/07

Keywords

  • Cost-sensitive classification
  • Fuzzy classification
  • Medical diagnosis
  • Pattern classification

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    Schaefer, G., Nakashima, T., Yokota, Y., & Ishibuchi, H. (2007). Cost-sensitive fuzzy classification for medical diagnosis. In 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007 (pp. 312-316). [4221238]