Cost-Sensitive Fuzzy Classification for Medical Diagnosis


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

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@article{Schaefer2007CostSensitiveFC, title={Cost-Sensitive Fuzzy Classification for Medical Diagnosis}, author={Gerald Schaefer and Tomoharu Nakashima and Yasuyuki Yokota and Hisao Ishibuchi}, journal={2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology}, year={2007}, pages={312-316} }