A New Medical Diagnosis Method Based on Z-Numbers

TL;DR

This paper introduces a novel decision-making approach for medical diagnosis using Z-numbers to better handle uncertainty, combining expert opinions and information fusion for improved accuracy.

Contribution

It presents a new methodology that represents expert opinions with Z-numbers, proposes a fuzzy number ranking method, and transforms Z-numbers into BPA for effective information fusion.

Findings

Effective in risk analysis and medical diagnosis

Improves decision accuracy through information fusion

Demonstrated efficiency in two experiments

Abstract

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.