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Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries
journal contribution
posted on 2020-06-14, 00:00 authored by Roohallah Alizadehsani, M Roshanzamir, Moloud Abdar, Adham Beykikhoshk, Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, P Plawiak, R S Tan, U R AcharyaCoronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus only on CAD detection and few studies have investigated stenosis of the right coronary artery (RCA), left circumflex (LCX) artery and left anterior descending (LAD) artery separately. Meanwhile, most of the datasets in this field are noisy (data uncertainty). However, to the best of our knowledge, there is no study conducted to address this important problem. This study uses the extension of the Z‐Alizadeh Sani dataset, containing 303 records with 54 features. A new feature selection algorithm is proposed in this work. Meanwhile, by discretization of data, we also handle the uncertainty in CAD prediction. To the best of our knowledge, this is the first study attempted to handle uncertainty in CAD prediction. Finally, the genetic algorithm (GA) is used to determine the hyper‐parameters of the support vector machine (SVM) kernels. We have achieved high accuracy for the stenosis diagnosis of each main coronary artery. The results of this study can aid the clinicians to validate their manual stenosis diagnosis of RCA, LCX and LAD coronary arteries.
History
Journal
Expert SystemsPagination
1 - 17Publisher
WileyLocation
London, Eng.Publisher DOI
ISSN
0266-4720eISSN
1468-0394Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
Keywords
coronary artery diseasediscretizationfeature selectionmachine learninguncertaintyScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Theory & MethodsComputer ScienceECG SIGNALSNEURAL-NETWORKDISEASESYSTEMMODELCLASSIFICATIONIDENTIFICATIONPERFORMANCEFEATURESArtificial Intelligence and Image Processing
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