NDLI: Design of a Fuzzy-based Decision Support System for Coronary Heart Disease Diagnosis

Content Provider	Springer Nature Link
Author	Lahsasna, Adel Ain, Raja or Zainuddin, Roziati Bulgiba, Awang
Copyright Year	2012
Abstract	In the present paper, a fuzzy rule-based system (FRBS) is designed to serve as a decision support system for Coronary heart disease (CHD) diagnosis that not only considers the decision accuracy of the rules but also their transparency at the same time. To achieve the two above mentioned objectives, we apply a multi-objective genetic algorithm to optimize both the accuracy and transparency of the FRBS. In addition and to help assess the certainty and the importance of each rule by the physician, an extended format of fuzzy rules that incorporates the degree of decision certainty and importance or support of each rule at the consequent part of the rules is introduced. Furthermore, a new way for employing Ensemble Classifiers Strategy (ECS) method is proposed to enhance the classification ability of the FRBS. The results show that the generated rules are humanly understandable while their accuracy compared favorably with other benchmark classification methods. In addition, the produced FRBS is able to identify the uncertainty cases so that the physician can give a special consideration to deal with them and this will result in a better management of efforts and tasks. Furthermore, employing ECS has specifically improved the ability of FRBS to detect patients with CHD which is desirable feature for any CHD diagnosis system.
Starting Page	3293
Ending Page	3306
Page Count	14
File Format	PDF
ISSN	01485598
Journal	Journal of Medical Systems
Volume Number	36
Issue Number	5
e-ISSN	1573689X
Language	English
Publisher	Springer US
Publisher Date	2012-01-18
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Coronary heart disease Fuzzy rule-based system Transparency Data mining Medical diagnosis Health Informatics Statistics for Life Sciences, Medicine, Health Sciences
Content Type	Text
Resource Type	Article
Subject	Health Information Management Information Systems Health Informatics

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