article

A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty

Authors:

Mohammad Shahadat Hossain,

Saifur Rahaman,

Rashed Mustafa,

Karl AnderssonAuthors Info & Claims

Soft Computing - A Fusion of Foundations, Methodologies and Applications, Volume 22, Issue 22

Pages 7571 - 7586

Published: 01 November 2018 Publication History

Abstract

Acute coronary syndrome (ACS) is responsible for the obstruction of coronary arteries, resulting in the loss of lives. The onset of ACS can be determined by looking at the various signs and symptoms of a patient. However, the accuracy of ACS determination is often put into question since there exist different types of uncertainties with the signs and symptoms. Belief rule-based expert systems (BRBESs) are widely used to capture uncertain knowledge and to accomplish the task of reasoning under uncertainty by employing belief rule base and evidential reasoning. This article presents the process of developing a BRBES to determine ACS predictability. The BRBES has been validated against the data of 250 patients suffering from chest pain. It is noticed that the outputs created from the BRBES are more dependable than that of the opinion of cardiologists as well as other two expert system tools, namely artificial neural networks and support vector machine. Hence, it can be argued that the BRBES is capable of playing an important role in decision making as well as in avoiding costly laboratory investigations. A procedure to train the system, allowing its enhancement of performance, is also presented.

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Cited By

Zhenjie ZXiaobin XPeng CXudong WXiaojian XGuodong WSchahram D(2021)A novel nonlinear causal inference approach using vector‐based belief rule baseInternational Journal of Intelligent Systems10.1002/int.2250036:9(5005-5027)Online publication date: 3-Jun-2021
https://dl.acm.org/doi/10.1002/int.22500

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cover image Soft Computing - A Fusion of Foundations, Methodologies and Applications

Soft Computing - A Fusion of Foundations, Methodologies and Applications Volume 22, Issue 22

November 2018

350 pages

ISSN:1432-7643

EISSN:1433-7479

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Copyright © Copyright © 2018 Springer-Verlag GmbH Germany, part of Springer Nature.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 November 2018

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Cited By

Zhenjie ZXiaobin XPeng CXudong WXiaojian XGuodong WSchahram D(2021)A novel nonlinear causal inference approach using vector‐based belief rule baseInternational Journal of Intelligent Systems10.1002/int.2250036:9(5005-5027)Online publication date: 3-Jun-2021
https://dl.acm.org/doi/10.1002/int.22500

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