Medical diagnosis support using uncertainty and imprecision measures

An algorithm of medical diagnosis support is proposed. It is based on the Dempster-Shafer theory of evidence, still the theory is extended for fuzzy focal elements. Definitions of the basic probability assignment as well as of belief and plausibility measures for the extension are provided. The proposed method makes it possible to interpret simultaneously numerous symptoms of different nature: crisp, fuzzy or parameters of undetermined domains. General rules of medical knowledge can be adapted to population dependencies. The algorithm's performance is verified for the thyroid gland diseases problem using data from an Internet base, simulated patient cases and real patient database. Accuracy of suggested diagnoses is considerably better for the proposed algorithm than for reference methods. The presented solutions are numerically easy and close to knowledge representation that is used in medical handbooks.