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Non-Invasive Detection of Coronary Artery Disease in High-Risk Patients Based on the Stenosis Prediction of Separate Coronary Arteries Publisher Pubmed



Alizadehsani R1 ; Hosseini MJ2 ; Khosravi A1 ; Khozeimeh F3 ; Roshanzamir M4 ; Sarrafzadegan N5 ; Nahavandi S1
Authors
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Authors Affiliations
  1. 1. Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, 3217, Australia
  2. 2. Department of Computer Science and Engineering, University of Washington, Seattle, United States
  3. 3. Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
  4. 4. Department of Electrical and Computer Engineering, Isfahan University of Technolgy, Isfahan, Iran
  5. 5. Isfahan University of Medical Sciences, Isfahan, Iran

Source: Computer Methods and Programs in Biomedicine Published:2018


Abstract

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. © 2018 Elsevier B.V.
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