A New Medical Diagnosis Method Based on Z-Numbers
This addresses uncertainty handling in medical diagnosis, but it appears incremental as it builds on existing Z-number and Dempster-Shafer theory frameworks.
The paper tackles uncertainty in medical diagnosis by proposing a new decision-making methodology based on Z-numbers, which transforms expert opinions into Basic Probability Assignment for information fusion, and demonstrates its efficiency through experiments in risk analysis and medical diagnosis.
How to handle uncertainty in medical diagnosis is an open issue. In this paper, a new decision making methodology based on Z-numbers is presented. Firstly, the experts' opinions are represented by Z-numbers. Z-number is an ordered pair of fuzzy numbers denoted as Z = (A, B). Then, a new method for ranking fuzzy numbers is proposed. And based on the proposed fuzzy number ranking method, a novel method is presented to transform the Z-numbers into Basic Probability Assignment (BPA). As a result, the information from different sources is combined by the Dempster' combination rule. The final decision making is more reasonable due to the advantage of information fusion. Finally, two experiments, risk analysis and medical diagnosis, are illustrated to show the efficiency of the proposed methodology.