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

DOI

10.2991/ijcis.2015.8.1.7How to use a DOI?

Keywords

Fuzzy sets, evidential reasoning, uncertainty, Dempster-Shafer theory, inclusion measure, medical diagnosis

Abstract

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yanni Wang
AU  - Yaping Dai
AU  - Yu-wang Chen
AU  - Fancheng Meng
PY  - 2015
DA  - 2015/01/01
TI  - The Evidential Reasoning Approach to Medical Diagnosis using Intuitionistic Fuzzy Dempster-Shafer Theory
JO  - International Journal of Computational Intelligence Systems
SP  - 75
EP  - 94
VL  - 8
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2015.8.1.7
DO  - 10.2991/ijcis.2015.8.1.7
ID  - Wang2015
ER  -