Fuzzy partitioning for clinical DSSs using statistical information transformed into possibility-based knowledge

Abstract

Fuzzy logic has gained much importance for its applications in Decision Support Systems (DSSs), especially in fields like medicine, where the final user has to handle uncertain data and vague concepts, and needs an intelligible system based on clear rule bases. In medical applications, physicians are often skilled to reason using statistical approaches, since this type of information is often known, or can be extracted from data. However, since decisions have to be applied to single patients, clinical data items have to be classified in order to obtain the plausibility of conclusions, rather than their probability. Therefore, statistical information can be used, in order to define fuzzy sets and rules for constructing DSSs based on fuzzy logic. While the transformation of probability distributions is well known in literature, here, an approach is presented for transforming likelihood functions into fuzzy sets, based on possibility theory, which is actually instanced into four different new methods for knowledge representation. A comparison among different methods is shown, as well as the analysis of transformation properties and resulting fuzzy sets characteristics are considered, by using synthetic and real data. Finally, some considerations about the most suitable method to be used in the context of clinical DSSs are given.