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

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