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Machine learning-based coronary artery disease diagnosis: a comprehensive review

journal contribution
posted on 2019-08-01, 00:00 authored by Roohallah Alizadehsani, Moloud Abdar, M Roshanzamir, Abbas KhosraviAbbas Khosravi, P M Kebria, Fahime KhozeimehFahime Khozeimeh, Saeid Nahavandi, N Sarrafzadegan, U R Acharya
Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, sample sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.

History

Journal

Computers in biology and medicine

Volume

111

Article number

103346

Pagination

1 - 14

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

0010-4825

eISSN

1879-0534

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2019, Elsevier Ltd.