A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment

In many engineering problems, we encounter vagueness in information and uncertainty in decision making, so as these phenomena cause we could not reach to certain results for our proposed solution. In this paper, a novel inference engine named fuzzy-evidential hybrid inference engine has been proposed using Dempster–Shafer theory of evidence and fuzzy sets theory. This hybrid engine operates in two phases. In the first phase, it models the input information’s vagueness through fuzzy sets. In following, extracting the fuzzy rule set for the problem, it applies the fuzzy inference rules on the acquired fuzzy sets to produce the first phase results. At second phase, the acquired results of previous stage are assumed as basic beliefs for the problem propositions and in this way, the belief and plausibility functions (or the belief interval) are set. Gathering information from different sources, they provide us with diverse basic beliefs which should be fused to produce an integrative result. For this purpose, evidential combination rules are used to perform the information fusion. Having applied the proposed engine on the coronary heart disease (CHD) risk assessment, it has yielded 91.58% accuracy rate for its correct prediction. This hybrid engine models the information’s vagueness and decision making’s uncertainty precisely and through information fusion, provides more accurate results, so as it could be considered as an intelligent decision support system in diverse engineering problems.