From Likelihood Uncertainty to Fuzziness: A Possibility-Based Approach for Building Clinical DSSs

For data classification, in fields like medicine, where vague concepts have to be considered, and where, at the same time, intelligible rules are required, research agrees on utility of fuzzy logic. In this ambit, if statistical information about the problem is known, or can be extracted from data, it can be used to define fuzzy sets and rules. Statistical knowledge can be acquired in terms of probability distributions or likelihood functions. Here, an approach is proposed for the transformation of likelihood functions into fuzzy sets, which considers possibility measure, and different methods arising from this approach are presented. By using real data, a comparison among different methods is performed, based on the analysis of transformation properties and resulting fuzzy sets characteristics. Finally, the best method to be used in the context of clinical decision support systems (DSSs) is chosen.