Coronary artery disease detection using computational intelligence methods Alizadehsani, Roohallah, Zangooei, Mohammad Hossein, Hosseini, Mohammad Javad, Habibi, Jafar, Khosravi, Abbas, Roshanzamir, Mohamad, Khozeimeh, Fahime, Sarrafzadegan, Nizal and Nahavandi, Saeid 2016, Coronary artery disease detection using computational intelligence methods, Knowledge-based systems, vol. 109, pp. 187-197, doi: 10.1016/j.knosys.2016.07.004. Attached Files Name Description MIMEType Size Downloads Title Coronary artery disease detection using computational intelligence methods Author(s) Alizadehsani, Roohallah Zangooei, Mohammad Hossein Hosseini, Mohammad Javad Habibi, Jafar Khosravi, Abbas orcid.org/0000-0001-6927-0744 Roshanzamir, Mohamad Khozeimeh, Fahime Sarrafzadegan, Nizal Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Journal name Knowledge-based systems Volume number 109 Start page 187 End page 197 Total pages 11 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2016-10-01 ISSN 0950-7051 Keyword(s) Coronary artery disease Support Vector Machine Information gain Kernel fusion Feature election Science & Technology Technology Computer Science, Artificial Intelligence Computer Science ARTIFICIAL NEURAL-NETWORKS DATA MINING APPROACH HEART-DISEASE MULTIOBJECTIVE OPTIMIZATION FEATURE-SELECTION RULE DISCOVERY DIAGNOSIS PREDICTION FEATURES Summary Nowadays, cardiovascular diseases are very common and are one of the main causes of death worldwide. One major type of such diseases is the coronary artery disease (CAD). The best and most accurate method for the diagnosis of CAD is angiography, which has significant complications and costs. Researchers are, therefore, seeking novel modalities for CAD diagnosis via data mining methods. To that end, several algorithms and datasets have been developed. However, a few studies have considered the stenosis of each major coronary artery separately. We attempted to achieve a high rate of accuracy in the diagnosis of the stenosis of each major coronary artery. Analytical methods were used to investigate the importance of features on artery stenosis. Further, a proposed classification model was built to predict each artery status in new visitors. To further enhance the models, a proposed feature selection method was employed to select more discriminative feature subsets for each artery. According to the experiments, accuracy rates of 86.14%, 83.17%, and 83.50% were achieved for the diagnosis of the stenosis of the left anterior descending (LAD) artery, left circumflex (LCX) artery and right coronary artery (RCA), respectively. To the best of our knowledge, these are the highest accuracy rates that have been obtained in the literature so far. In addition, a number of rules with high confidence were introduced for deciding whether the arteries were stenotic or not. Also, we applied the proposed method on two challenging datasets and obtained the best accuracy in comparison with other methods. Language eng DOI 10.1016/j.knosys.2016.07.004 Field of Research 099999 Engineering not elsewhere classified 08 Information And Computing Sciences 15 Commerce, Management, Tourism And Services 17 Psychology And Cognitive Sciences Socio Economic Objective 0 Not Applicable HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2016, Elsevier B.V. Persistent URL http://hdl.handle.net/10536/DRO/DU:30089960 Document type: Journal Article Collections: Centre for Intelligent Systems Research 2018 ERA Submission Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 05 Dec 2016, 13:23:44 EST Tue, 06 Dec 2016, 04:01:36 EST Tue, 06 Dec 2016, 10:20:38 EST Thu, 30 Mar 2017, 05:04:40 EST Wed, 07 Jun 2017, 11:25:23 EST Wed, 07 Jun 2017, 11:28:56 EST Thu, 15 Jun 2017, 08:13:38 EST Filtered Full Citation counts:   Cited 7 times in TR Web of Science Cited 9 times in Scopus Search Google Scholar Access Statistics: 216 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 05 Dec 2016, 13:23:44 EST

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