Combining uncertainty and imprecision in models of medical diagnosis

The paper presents a unified fuzzy-probabilistic framework for modeling processes of medical diagnosis. The two basic concepts of the Dempster-Shafer theory, i.e. focal elements and a basic probability assignment, correspond to disease symptoms and the significance of an individual symptom in the diagnosis, respectively. The belief computation is related to diagnostic inference. The final conclusion of the inference is the diagnosis with the greatest belief value. Fuzzy sets are used to describe focal elements. It is shown how their membership functions and basic probability assignments are estimated on the basis of experimental data. The interpretation of focal elements as fuzzy sets along with individual consideration of evidence imprecision and uncertainty of diagnosis are the essential new aspects of the presented method. Experimental studies have demonstrated the superiority of the proposed approach over some other modeling alternatives.