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Weighted fuzzy classification with integrated learning method for medical diagnosis

Nakashima, T., Schaefer, G., Yokota, Y., Zhu, Shao Ying and Ishibuchi, H. (2005) Weighted fuzzy classification with integrated learning method for medical diagnosis. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, pp. 5623-5626

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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.

Item Type: Book Section
Status: Published
DOI: https://doi.org/10.1109/IEMBS.2005.1615761
School/Department: School of Science, Technology and Health
URI: https://ray.yorksj.ac.uk/id/eprint/9960

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