A new medical diagnosis method based on Z-numbers

Dong Wu; Xiang Liu; Feng Xue; Hanqing Zheng; Yehang Shou; Wen Jiang

doi:10.1007/s10489-017-1002-4

A new medical diagnosis method based on Z-numbers

Dong Wu, Xiang Liu, Feng Xue, Hanqing Zheng, Yehang Shou, Wen Jiang

School of Electronics and Information

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

Abstract

Handling uncertainty in medical diagnoses is an open issue. In this paper, we present a new decision-making methodology based on Z-numbers. First, experts’ opinions are represented by Z-numbers. A Z-number is an ordered pair of fuzzy numbers denoted by Z = (A, B). We then propose a new method for ranking fuzzy numbers. Based on this ranking method, we present a novel method for transforming Z-numbers into basic probability assignments. This allows information from different sources to be combined by Dempster’s combination rule, and results in more reasonable decision making because of the advantages of information fusion. Finally, two experiments on risk analysis and medical diagnosis illustrate the efficiency of the proposed methodology.

Original language	English
Pages (from-to)	854-867
Number of pages	14
Journal	Applied Intelligence
Volume	48
Issue number	4
DOIs	https://doi.org/10.1007/s10489-017-1002-4
State	Published - 1 Apr 2018

Keywords

Decision making
Dempster-Shafer evidence theory
Fuzzy numbers
Medical diagnosis
Ranking Z-numbers
Risk assessment
Z-numbers

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10.1007/s10489-017-1002-4

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title = "A new medical diagnosis method based on Z-numbers",

abstract = "Handling uncertainty in medical diagnoses is an open issue. In this paper, we present a new decision-making methodology based on Z-numbers. First, experts{\textquoteright} opinions are represented by Z-numbers. A Z-number is an ordered pair of fuzzy numbers denoted by Z = (A, B). We then propose a new method for ranking fuzzy numbers. Based on this ranking method, we present a novel method for transforming Z-numbers into basic probability assignments. This allows information from different sources to be combined by Dempster{\textquoteright}s combination rule, and results in more reasonable decision making because of the advantages of information fusion. Finally, two experiments on risk analysis and medical diagnosis illustrate the efficiency of the proposed methodology.",

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AU - Liu, Xiang

AU - Xue, Feng

AU - Zheng, Hanqing

AU - Shou, Yehang

AU - Jiang, Wen

PY - 2018/4/1

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AB - Handling uncertainty in medical diagnoses is an open issue. In this paper, we present a new decision-making methodology based on Z-numbers. First, experts’ opinions are represented by Z-numbers. A Z-number is an ordered pair of fuzzy numbers denoted by Z = (A, B). We then propose a new method for ranking fuzzy numbers. Based on this ranking method, we present a novel method for transforming Z-numbers into basic probability assignments. This allows information from different sources to be combined by Dempster’s combination rule, and results in more reasonable decision making because of the advantages of information fusion. Finally, two experiments on risk analysis and medical diagnosis illustrate the efficiency of the proposed methodology.

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A new medical diagnosis method based on Z-numbers

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Keywords

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