Welcome to the InfoSci Platform
Reference Hub5
Assessment of Clinical Decision Support Systems for Predicting Coronary Heart Disease

Assessment of Clinical Decision Support Systems for Predicting Coronary Heart Disease

Sidahmed Mokeddem, Baghdad Atmani
Copyright: © 2016 |Volume: 7 |Issue: 3 |Pages: 17
ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781466691308|DOI: 10.4018/IJORIS.2016070104
Cite Article Cite Article

MLA

Mokeddem, Sidahmed, and Baghdad Atmani. "Assessment of Clinical Decision Support Systems for Predicting Coronary Heart Disease." IJORIS vol.7, no.3 2016: pp.57-73. http://doi.org/10.4018/IJORIS.2016070104

APA

Mokeddem, S. & Atmani, B. (2016). Assessment of Clinical Decision Support Systems for Predicting Coronary Heart Disease. International Journal of Operations Research and Information Systems (IJORIS), 7(3), 57-73. http://doi.org/10.4018/IJORIS.2016070104

Chicago

Mokeddem, Sidahmed, and Baghdad Atmani. "Assessment of Clinical Decision Support Systems for Predicting Coronary Heart Disease," International Journal of Operations Research and Information Systems (IJORIS) 7, no.3: 57-73. http://doi.org/10.4018/IJORIS.2016070104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The use of data mining approaches in medicine and medical science has become necessary especially with the evolution of these approaches and their contributions medical decision support. Coronary artery disease (CAD) touches millions of people all over the world including a major portion in Algeria. However, much advancement has been done in medical science, but the early detection of CAD is still a challenge for prevention. Although, the early detection of CAD is a prevention challenge for clinicians. The subject of this paper is to propose new clinical decision support system (CDSS) for evaluating risk of CAD called CADSS. In this paper, the authors describe the characteristics of clinical decision support systems CDSSs for the diagnosis of CAD. The aim of this study is to explain the clinical contribution of CDSSs for medical decision-making and compare data mining techniques used for their implementation. Then, they describe their new fuzzy logic-based approach for detecting CAD at an early stage. Rules were extracted using a data mining technique and validated by experts, and the fuzzy expert system was used to handle the uncertainty present in the medical field. This work presents the main risk factors responsible for CAD and presents the designed CASS. The developed CADSS leads to 94.05% of accuracy, and its effectiveness was compared with different CDSS.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.