Hybrid genetic‐discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries

TitleHybrid genetic‐discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries
Publication TypeJournal Article
Year of Publication2020
AuthorsAlizadehsani R, Roshanzamir M, Abdar M, Beykikhoshk A, Khosravi A, Nahavandi S, Pławiak P, San Tan R, U Acharya R
JournalWiley, Expert Systems
Date Published06/2020
ISSN1468-0394
KeywordsCoronary artery disease, Discretization, Feature selection, Machine learning, Uncertainty
Abstract

Coronary 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.

URLhttps://doi.org/10.1111/exsy.12573
DOI10.1111/exsy.12573