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

Yanni Wang; Yaping Dai; Yu wang Chen; Fancheng Meng

doi:10.2991/ijcis.2015.8.1.7

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

Yanni Wang^*, Yaping Dai, Yu wang Chen, Fancheng Meng

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自动化学院

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1 引用（Scopus）

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.

源语言	英语
页（从-至）	75-94
页数	20
期刊	International Journal of Computational Intelligence Systems
卷	8
期	1
DOI	https://doi.org/10.2991/ijcis.2015.8.1.7
出版状态	已出版 - 1月 2015

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10.2991/ijcis.2015.8.1.7

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Wang, Y., Dai, Y., Chen, Y. W., & Meng, F. (2015). The Evidential Reasoning Approach to Medical Diagnosis using Intuitionistic Fuzzy Dempster-Shafer Theory. International Journal of Computational Intelligence Systems, 8(1), 75-94. https://doi.org/10.2991/ijcis.2015.8.1.7

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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.",

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

author = "Yanni Wang and Yaping Dai and Chen, {Yu wang} and Fancheng Meng",

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doi = "10.2991/ijcis.2015.8.1.7",

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AB - 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.

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

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