{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T21:17:00Z","timestamp":1788383820743,"version":"build-2803163510"},"reference-count":41,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Coronary artery disease (CAD) is known as the most common cardiovascular disease. The development of CAD is influenced by several risk factors. Diagnostic and therapeutic methods of this disease have many and costly side effects. Therefore, researchers are looking for cost-effective and accurate methods to diagnose this disease. Machine learning algorithms can help specialists diagnose the disease early. The aim of this study is to detect CAD using machine learning algorithms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>In this study, three data mining algorithms support vector machine (SVM), artificial neural network (ANN), and random forest were used to predict CAD using the Isfahan Cohort Study dataset of Isfahan Cardiovascular Research Center. 19 features with 11495 records from this dataset were used for this research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>All three algorithms achieved relatively close results. However, the SVM had the highest accuracy compared to the other techniques. The accuracy was calculated as 89.73% for SVM. The ANN algorithm also obtained the high area under the curve, sensitivity and accuracy and provided acceptable performance. Age, sex, Sleep satisfaction, history of stroke, history of palpitations, and history of heart disease were most correlated with target class. Eleven rules were also extracted from this dataset with high confidence and support.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>In this study, it was shown that machine learning algorithms can be used with high accuracy to detect CAD. Thus, it allows physicians to perform timely preventive treatment in patients with CAD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/abr.abr_383_21","type":"journal-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T02:18:03Z","timestamp":1698373083000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":23,"title":["Diagnosis of Coronary Artery Disease Based on Machine Learning Algorithms Support Vector Machine, Artificial Neural Network, and Random Forest"],"prefix":"10.4103","volume":"12","author":[{"given":"Saeed","family":"Saeedbakhsh","sequence":"first","affiliation":[{"name":"Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Sattari","sequence":"additional","affiliation":[{"name":"Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maryam","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"Department of Management and Health Information Technology, School of Management and Medical Information Sciences, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamshid","family":"Najafian","sequence":"additional","affiliation":[{"name":"Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farzaneh","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2023,2]]},"reference":[{"key":"R1-20240830","doi-asserted-by":"crossref","first-page":"2016282","DOI":"10.1155\/2018\/2016282","article-title":"Automated diagnosis of coronary artery disease: A review and workflow","volume":"2018","author":"Mastoi","year":"2018","journal-title":"Cardiol Res Pract"},{"key":"R2-20240830","doi-asserted-by":"crossref","first-page":"104095","DOI":"10.1016\/j.compbiomed.2020.104095","article-title":"Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020","volume":"128","author":"Alizadehsani","year":"2021","journal-title":"Comput Biol Med"},{"key":"R3-20240830","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.cmpb.2013.03.004","article-title":"A data mining approach for diagnosis of coronary artery disease","volume":"111","author":"Alizadehsani","year":"2013","journal-title":"Comput Methods Programs Biomed"},{"key":"R4-20240830","doi-asserted-by":"crossref","first-page":"100255","DOI":"10.1016\/j.imu.2019.100255","article-title":"Computer-aided decision-making for predicting liver disease using PSO-based optimized SVM with feature selection","volume":"17","author":"Hassannataj Joloudari","year":"2019","journal-title":"Inform Med Unlocked"},{"key":"R5-20240830","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.hlpt.2020.05.002","article-title":"Telemedicine against CoVID-19 crisis","volume":"9","author":"Ajami","year":"2020","journal-title":"Health Policy Technol"},{"key":"R6-20240830","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.tele.2018.11.007","article-title":"Identification of significant features and data mining techniques in predicting heart disease","volume":"36","author":"Amin","year":"2019","journal-title":"Telemat Inform"},{"key":"R7-20240830","doi-asserted-by":"crossref","first-page":"731","DOI":"10.3390\/ijerph17030731","article-title":"Coronary artery disease diagnosis; 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