A Hybrid Fuzzy Soft Sets Decision Making Method in Medical Diagnosis

Abstract

The existing approaches for fuzzy soft sets decision-making are mainly based on different types of level soft sets. How to deal with such kinds of fuzzy soft sets decision-making problems via decreasing the uncertainty resulting from human's subjective cognition is still an open issue. To address this issue, a hybrid method for utilizing fuzzy soft sets in decision-making by integrating a fuzzy preference relations analysis based on the belief entropy with the Dempster-Shafer evidence theory is proposed. The proposed method is composed of four procedures. First, we measure the uncertainties of parameters by leveraging the belief entropy. Second, with the fuzzy preference relations analysis, the uncertainties of parameters are modulated by making use of the relative reliability preference of parameters. Third, an appropriate basic probability assignment in terms of each parameter is generated on the modulated uncertainty degrees of parameters basis. Finally, we adopt Dempster's combination rule to fuse the independent parameters into an integrated one; thus, the best one can be obtained based on the ranking candidate alternatives. In order to validate the feasibility and effectiveness of the proposed method, a numerical example and a medical diagnosis application are implemented. From the experimental results, it is demonstrated that the proposed method outperforms the related methods, because the uncertainty resulting from human's subjective cognition can be reduced; meanwhile, the decision-making level can also be improved with better performance.

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