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.