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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.

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Title Coronary artery disease detection using computational intelligence methods
Author(s) Alizadehsani, Roohallah
Zangooei, Mohammad Hossein
Hosseini, Mohammad Javad
Habibi, Jafar
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Roshanzamir, Mohamad
Khozeimeh, Fahime
Sarrafzadegan, Nizal
Nahavandi, Saeid
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
Collection: Centre for Intelligent Systems Research
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