The Evidential Reasoning Approach to Medical Diagnosis using Intuitionistic Fuzzy Dempster-Shafer Theory

被引：25

作者：

Wang, Yanni ^{[1
]}

Dai, Yaping ^{[1
]}

Chen, Yu-wang ^{[2
]}

Meng, Fancheng ^{[1
]}

机构：

[1] Beijing Inst Technol, Sch Automat, Beijing 100081, Haidian Distric, Peoples R China

[2] Manchester Business Sch, Decis & Cognit Sci Res Ctr, Manchester M15 6PB, Lancs, England

来源：

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS | 2015年 / 8卷 / 01期

关键词：

medical diagnosis; inclusion measure; Dempster-Shafer theory; uncertainty; evidential reasoning; Fuzzy sets; ATTRIBUTE DECISION-ANALYSIS; BASE INFERENCE METHODOLOGY; FRAMEWORK; UNCERTAINTY; SETS;

D O I：

10.1080/18756891.2014.964009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

For medical diagnosis, fuzzy Dempster-Shafer theory is extended to model domain knowledge under probabilistic and fuzzy uncertainty. However, there are some information loss using discrete fuzzy sets and traditional matching degree method. This study aims to provide a new evidential structure to reduce information loss. This paper proposes a new intuitionistic fuzzy evidential reasoning (IFER) approach which combines intuitionistic trapezoidal fuzzy numbers and inclusion measure to improve the accuracy of representation and reasoning. The proposed approach has been validated by a stroke diagnosis. It is shown that the IFER approach leads to more accurate results.

引用

页码：75 / 94

页数：20

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