An Analysis of Five Strategies for Reasoning in Uncertainties and Their Suitability for Pathology

Abstract

In reasoning systems, uncertainty plays a crucial part, especially for those fields where judgements are essential, as in pathology. Uncertainty has several aspects, such as prevalence of diseases, occurrence of findings and the predictive value of findings. For the functioning of a reasoning system two aspects are crucial: first, the internal representation of the uncertainty and second, the way in which the uncertainty is propagated in the reasoning process when combining formal statements. Five well known reasoning strategies are compared: probability theory, MYCIN'S certainty factor model, fuzzy set theory, the theory of Dempster-Shafer and the scoring scheme of Internist. The comparison addresses, among others, the following questions: - Can the different aspects of uncertainty be dealt with as separate entities? - How are unknown uncertainties dealt with? - How is evidence in favor of a hypothesis combined with evidence against it? - How does the model treat the simultanuous occurrence of more than one disorder, that is, how does the model support reasoning with compound hypotheses? It is preliminarly concluded, that only in Internist and probability theory, the different aspects of uncertainty are expressed as separate entities. Hence, the other models do not accurately represent uncertain knowledge. Also, theoretically attractive models such as Bayes, MYCIN and the theory of Dempster-Shafer can only function properly under the tight condition of mutual exclusiveness of hypotheses, not always suited for broader parts of pathology. They may, however, be suited for smaller parts with a limited number of defined diseases and a limited number of features. All models but Bayes lack a predictable performance as there is no or only a partial underlying theory to guarantee minimization of the overall error.