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Model uncertainty quantification for diagnosis of each main coronary artery stenosis
journal contribution
posted on 2020-07-01, 00:00 authored by Roohallah Alizadehsani, M Roshanzamir, Moloud Abdar, Adham Beykikhoshk, M H Zangooei, Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, R S Tan, U R AcharyaOne of the main causes of death in the world is coronary artery disease (CAD). CAD occurs when there is stenosis in one or more of the three major coronary arteries: right coronary artery (RCA), left circumflex (LCX) artery, and left anterior descending (LAD) artery. The gold standard or CAD diagnosis is angiography, but it is invasive, costly, and time consuming. Therefore, researchers continually seek new machine learning methods that can screen for CAD non-invasively. For reliable and cost-effective CAD diagnosis, several algorithms have been developed. Most prior studies analyzed the presence or absence of CAD in a dichotomous manner. Herein, we studied the more complex problem of classification of stenosis in individual LAD, LCX, and RCA by applying machine learning algorithms on the Z-Alizadeh Sani dataset that comprised 303 subjects, each with 54 features. In addition, our new methodology is developed to handle model uncertainty in the prediction of individual artery stenosis. It uses the hyperplane distance from a sample and accuracy rate of the classifier during the training phase to enhance its performance. Our results demonstrate high diagnostic performance of the proposed method for diagnosis of stenosis in individual RCA, LCX, and LAD, achieving accuracy rates of 82.67%, 83.67% and 86.43%, respectively. This is the best performance of ML techniques applied to the Z-Alizadeh Sani dataset.
History
Journal
Soft ComputingVolume
24Issue
13Pagination
10149 - 10160Publisher
SpringerLocation
Berlin, GermanyPublisher DOI
ISSN
1432-7643eISSN
1433-7479Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2019, Springer-Verlag GmbH Germany, part of Springer Nature.Usage metrics
Keywords
Data miningMachine learningCoronary artery diseaseSVMGini indexLADFeature selectionScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Interdisciplinary ApplicationsComputer ScienceNEURAL-NETWORKEXPERT-SYSTEM2ND OPINIONSDISEASEArtificial Intelligence and Image Processing
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