Medical Differential Diagnosis through Type-2 Fuzzy Sets

In this paper we present a model of differential medical diagnosis, based on type 2 fuzzy sets. Given the patient history case and a set of suspected-inherent pathologies, the method creates a set of linguistically labelled clusters of the pathologies. The pathologies present in each cluster are ordered through a similarity function. In the case of lack of information, our method indicates the elements needed to have a more precise diagnosis and it can control its same accuracy