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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 AcharyaCoronary 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 medicineVolume
111Article number
103346Pagination
1 - 14Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0010-4825eISSN
1879-0534Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2019, Elsevier Ltd.Usage metrics
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No categories selectedKeywords
CAD diagnosisMachine learningData miningFeature selectionScience & TechnologyLife Sciences & BiomedicineTechnologyBiologyComputer Science, Interdisciplinary ApplicationsEngineering, BiomedicalMathematical & Computational BiologyLife Sciences & Biomedicine - Other TopicsComputer ScienceEngineeringARTIFICIAL NEURAL-NETWORKDATA MINING APPROACHHEART-DISEASELOGISTIC-REGRESSIONDECISION-MAKINGEXPERT-SYSTEMECG SIGNALSMYOCARDIAL-INFARCTIONAUTOMATED DIAGNOSISFEATURE-SELECTION
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