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Non-invasive detection of coronary artery disease in high-risk patients based on the stenosis prediction of separate coronary arteries

journal contribution
posted on 2018-08-01, 00:00 authored by Roohallah Alizadehsani, Mohammad Javad Hosseini, Abbas KhosraviAbbas Khosravi, Fahime KhozeimehFahime Khozeimeh, Mohamad Roshanzamir, Nizal Sarrafzadegan, Saeid Nahavandi
BACKGROUND AND OBJECTIVE: Cardiovascular diseases are an extremely widespread sickness and account for 17 million deaths in the world per annum. Coronary artery disease (CAD) is one of such diseases with an annual mortality rate of about 7 million. Thus, early diagnosis of CAD is of vital importance. Angiography is currently the modality of choice for the detection of CAD. However, its complications and costs have prompted researchers to seek alternative methods via machine learning algorithms. METHODS: The present study proposes a novel machine learning algorithm. The proposed algorithm uses three classifiers for detection of the stenosis of three coronary arteries, i.e., left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) to get higher accuracy for CAD diagnosis. RESULTS: This method was applied on the extension of Z-Alizadeh Sani dataset which contains demographic, examination, ECG, and laboratory and echo data of 500 patients. This method achieves an accuracy, sensitivity and specificity rates of 96.40%, 100% and 88.1%, respectively for the detection of CAD. To our knowledge, such high rates of accuracy and sensitivity have not been attained elsewhere before. CONCLUSION: This new algorithm reliably distinguishes those with normal coronary arteries from those with CAD which may obviate the need for angiography in the normal group.

History

Journal

Computer methods and programs in biomedicine

Volume

162

Pagination

119 - 127

Publisher

Elsevier

Location

Amsterdam, The Netherlands

eISSN

1872-7565

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2018, Elsevier