Hybridization of possibility theory and supervised clustering to build DSSs for classification in medicine

Fuzzy-based Decision Support Systems (DSSs) have gained increasing importance in medicine, since they rely on a transparent and interpretable rule base. A very attractive feature for these systems is to present their results as a set of plausible conclusions, each of them associated with a degree of possibility. In order to face this need, this work proposes a novel approach consisting in hybridization of possibility theory and a classical fuzzy clustering method, based on a distance metric interpretable in a probabilistic framework with the final aim of determining both fuzzy rules and partitions. As a proof of concept, the method has been applied to a real-life application pertaining the classification of Multiple Sclerosis Lesions. Finally, some sophistications are proposed for a future refinement, in order to improve the quality of results and the generality of applications.