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A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty
University of Chittagong, Bangladesh.ORCID iD: 0000-0002-7473-8185
International Islamic University Chittagong.
University of Chittagong, Bangladesh.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
2017 (English)In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479Article in journal (Refereed) Epub ahead of print
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2017.
Keyword [en]
Acute coronary syndrome (ACS), Expert system, Belief rule base, Suspicion, Signs and symptoms, Uncertainty
National Category
Computer and Information Science Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-64893DOI: 10.1007/s00500-017-2732-2OAI: oai:DiVA.org:ltu-64893DiVA: diva2:1127806
Projects
BRBWSN
Funder
Swedish Research Council, 2014-4251
Available from: 2017-07-19 Created: 2017-07-19 Last updated: 2017-08-17

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