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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

13 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)7571–7586
Number of pages16
Journal / PublicationSoft Computing
Volume22
Issue number22
Online published19 Jul 2017
Publication statusPublished - Nov 2018
Externally publishedYes

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.

Research Area(s)

  • Acute coronary syndrome (ACS), Expert system, Belief rule base, Suspicion, Signs and symptoms, Uncertainty

Citation Format(s)

A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty. / Hossain, Mohammad Shahadat ; Rahaman, Saifur; Mustafa, Rashed ; Andersson, Karl.

In: Soft Computing, Vol. 22, No. 22, 11.2018, p. 7571–7586.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review