The paper presents a study on data-driven diagnostic rules, which are easy to interpret by human experts. To this end, the Dempster-Shafer theory extended for fuzzy focal elements is used. Premises of the rules (fuzzy focal elements) are provided by membership functions which shapes are changing according to input symptoms. The main aim of the present study is to evaluate common membership function shapes and to introduce a rule elimination algorithm. Proposed methods are first illustrated with the popular Iris data set. Next experiments with five medical benchmark databases are performed. Results of the experiments show that various membership function shapes provide different inference efficiency but the extracted rule sets are close to each other. Thus indications for determining rules with possible heuristic interpretation can be formulated.
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The paper presents organization and performance of the module structure expert system for medicine and discussion on certain information technology problems connected with building and exploitation of the system. The considered problems mainly deal with knowledge acquisition automation and hypermedia applications for well communication. After discussion some remarks on conception of knowledge acquisition and dialog organization as well as some conclutions relative to their implementability and utilizability are also given.
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