{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T03:48:42Z","timestamp":1773114522426,"version":"3.50.1"},"reference-count":25,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T00:00:00Z","timestamp":1668470400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"crossref"}]},{"name":"International Cooperative R&D Program","award":["1415166057"],"award-info":[{"award-number":["1415166057"]}]},{"name":"Basic Science Research Program, National Research Foundation of Korea, Ministry of Education","award":["NRF-2020R1I1A1A01071884"],"award-info":[{"award-number":["NRF-2020R1I1A1A01071884"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Acute coronary syndrome (ACS) has been one of the most important issues in global public health. The high recurrence risk of patients with coronary heart disease (CHD) has led to the importance of post-discharge care and secondary prevention of CHD. Previous studies provided binary results of ACS recurrence risk; however, studies providing the recurrence risk of an individual patient are rare. In this study, we conducted a model which provides the recurrence risk probability for each patient, along with the binary result, with two datasets from the Korea Health Insurance Review and Assessment Service and Chungbuk National University Hospital. The total data of 6,535 patients who had been diagnosed with ACS were used to build a machine learning model by using logistic regression. Data including age, gender, procedure codes, procedure reason, prescription drug codes, and condition codes were used as the model predictors. The model performance showed 0.893, 0.894, 0.851, 0.869, and 0.921 for accuracy, precision, recall, F1-score, and AUC, respectively. Our model provides the ACS recurrence probability of each patient as a personalized ACS recurrence risk, which may help motivate the patient to reduce their own ACS recurrence risk. The model also shows that acute transmural myocardial infarction of an unspecified site, and other sites and acute transmural myocardial infarction of an unspecified site contributed most significantly to ACS recurrence with an odds ratio of 97.908 as a procedure reason code and with an odds ratio of 58.215 as a condition code, respectively.<\/jats:p>","DOI":"10.7717\/peerj.14348","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T08:13:43Z","timestamp":1668500023000},"page":"e14348","source":"Crossref","is-referenced-by-count":5,"title":["Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study"],"prefix":"10.7717","volume":"10","author":[{"given":"Vungsovanreach","family":"Kong","sequence":"first","affiliation":[{"name":"Department of Big Data, Chungbuk National University, Cheongju, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oui","family":"Somakhamixay","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, Chungbuk National University, Cheongju, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wan-Sup","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, Chungbuk National University, Cheongju, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gilwon","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Health Informatics and Management, College of Medicine, Chungbuk National University, Cheongju, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heesun","family":"Won","sequence":"additional","affiliation":[{"name":"Cybrebain Research Section, Electronics and Telecommunications Research Institute, Daejeon, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"HyungChul","family":"Rah","sequence":"additional","affiliation":[{"name":"Research Institute of Veterinary Medicine, Chungbuk National University, Cheongju, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heui Je","family":"Bang","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, College of Medicine, Chungbuk National University, Cheongju,  South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"4443","published-online":{"date-parts":[[2022,11,15]]},"reference":[{"key":"10.7717\/peerj.14348\/ref-1","doi-asserted-by":"publisher","first-page":"3966","DOI":"10.1038\/s41467-020-17804-2","article-title":"Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram","volume":"11","author":"Al-Zaiti","year":"2020","journal-title":"Nature Communications"},{"key":"10.7717\/peerj.14348\/ref-2","first-page":"217","article-title":"Convergence study in development of severity adjustment method for death with acute myocardial infarction patients using machine learning","volume":"17","author":"Baek","year":"2019","journal-title":"Journal of Digital Convergence"},{"key":"10.7717\/peerj.14348\/ref-3","doi-asserted-by":"publisher","first-page":"e56-e528","DOI":"10.1161\/cir.0000000000000659","article-title":"Heart disease and stroke statistics\u20142019 update: a report from the American Heart Association","volume":"139","author":"Benjamin","year":"2019","journal-title":"Circulation"},{"key":"10.7717\/peerj.14348\/ref-4","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/s10916-016-0432-6","article-title":"Diagnosis of acute coronary syndrome with a support vector machine","volume":"40","author":"Berikol","year":"2016","journal-title":"Journal of Medical Systems"},{"key":"10.7717\/peerj.14348\/ref-5","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1111\/anec.12130","article-title":"ECG diagnosis and classification of acute coronary syndromes","volume":"19","author":"Birnbaum","year":"2014","journal-title":"Annals of Noninvasive Electrocardiology"},{"key":"10.7717\/peerj.14348\/ref-6","first-page":"107","article-title":"Population trends of recurrent coronary heart disease event rates remain high","volume":"4","author":"Briffa","year":"2011","journal-title":"Circulation: Cardiovascular Quality and Outcomes"},{"key":"10.7717\/peerj.14348\/ref-7","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"Journal of Artificial Intelligence Research"},{"key":"10.7717\/peerj.14348\/ref-8","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.ahj.2005.03.034","article-title":"Determinants of referral to cardiac rehabilitation programs in patients with coronary artery disease: a systematic review","volume":"151","author":"Cort\u00e9s","year":"2006","journal-title":"American Heart Journal"},{"key":"10.7717\/peerj.14348\/ref-9","doi-asserted-by":"publisher","first-page":"e300","DOI":"10.1016\/S2589-7500(22)00025-5","article-title":"Validation of the myocardial-ischaemic-injury-index machine learning algorithm to guide the diagnosis of myocardial infarction in a heterogenous population: a prespecified exploratory analysis","volume":"4","author":"Doudesis","year":"2022","journal-title":"The Lancet Digital Health"},{"key":"10.7717\/peerj.14348\/ref-10","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1097\/00008483-200007000-00005","article-title":"Barriers to outpatient cardiac rehabilitation participation and adherence","volume":"20","author":"Evenson","year":"2000","journal-title":"Journal of Cardiopulmonary Rehabilitation and Prevention"},{"key":"10.7717\/peerj.14348\/ref-11","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1613\/jair.1.11192","article-title":"SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary","volume":"61","author":"Fern\u00e1ndez","year":"2018","journal-title":"Journal of Artificial Intelligence Research"},{"key":"10.7717\/peerj.14348\/ref-12","doi-asserted-by":"publisher","first-page":"A207","DOI":"10.1161\/circoutcomes.10.suppl_3.207","article-title":"Abstract 207: burden of cardiovascular disease (CVD) on economic cost, comparison of outcomes in US and Europe","volume":"10","author":"Giedrimiene","year":"2017","journal-title":"Circulation: Cardiovascular Quality and Outcomes"},{"key":"10.7717\/peerj.14348\/ref-13","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1097\/00008483-200501000-00006","article-title":"Patient preferences for home-based versus hospital-based cardiac rehabilitation","volume":"25","author":"Grace","year":"2005","journal-title":"Journal of Cardiopulmonary Rehabilitation and Prevention"},{"key":"10.7717\/peerj.14348\/ref-14","doi-asserted-by":"crossref","DOI":"10.4135\/9781412984515","volume-title":"Interaction effects in logistic regression","author":"Jaccard","year":"2001"},{"key":"10.7717\/peerj.14348\/ref-15","doi-asserted-by":"publisher","first-page":"938","DOI":"10.5124\/jkma.2016.59.12.938","article-title":"Overview of cardiac rehabilitation","volume":"59","author":"Kim","year":"2016","journal-title":"Journal of the Korean Medical Association"},{"key":"10.7717\/peerj.14348\/ref-16","first-page":"9","article-title":"Random forest vs logistic regression: binary classification for heterogeneous datasets","volume":"1","author":"Kirasich","year":"2018","journal-title":"SMU Data Science Review"},{"key":"10.7717\/peerj.14348\/ref-17","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.1177\/2047487317710803","article-title":"Clinical and cost-effectiveness of home-based cardiac rehabilitation compared to conventional, centre-based cardiac rehabilitation: results of the FIT@ Home study","volume":"24","author":"Kraal","year":"2017","journal-title":"European Journal of Preventive Cardiology"},{"key":"10.7717\/peerj.14348\/ref-18","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1097\/01.NAJ.0000351508.39509.e2","article-title":"Acute coronary syndrome","volume":"109","author":"Overbaugh","year":"2009","journal-title":"AJN, American Journal of Nursing"},{"key":"10.7717\/peerj.14348\/ref-19","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.hlc.2016.07.008","article-title":"Management of acute coronary syndromes in geriatric patients","volume":"26","author":"Padilla","year":"2017","journal-title":"Heart, Lung and Circulation"},{"issue":"2","key":"10.7717\/peerj.14348\/ref-20","first-page":"49","article-title":"Development of home monitoring service for cardiac rehabilitation patients and user satisfaction evaluation","volume":"30","author":"Rah","year":"2020","journal-title":"Chungbuk Medical Journal"},{"key":"10.7717\/peerj.14348\/ref-21","doi-asserted-by":"publisher","first-page":"21","DOI":"10.5694\/j.1326-5377.2007.tb00783.x","article-title":"High risk-factor level and low risk-factor knowledge in patients not accessing cardiac rehabilitation after acute coronary syndrome","volume":"186","author":"Redfern","year":"2007","journal-title":"Medical Journal of Australia"},{"key":"10.7717\/peerj.14348\/ref-22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1742-4682-10-1","article-title":"Application of pattern recognition tools for classifying acute coronary syndrome: an integrated medical modeling","volume":"10","author":"Salari","year":"2013","journal-title":"Theoretical Biology and Medical Modelling"},{"key":"10.7717\/peerj.14348\/ref-23","first-page":"227","article-title":"Explaining odds ratios","volume":"19","author":"Szumilas","year":"2010","journal-title":"Journal of the Canadian Academy of Child and Adolescent Psychiatry"},{"key":"10.7717\/peerj.14348\/ref-24","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1016\/j.jacc.2008.01.030","article-title":"Current status of cardiac rehabilitation","volume":"51","author":"Wenger","year":"2008","journal-title":"Journal of the American College of Cardiology"},{"key":"10.7717\/peerj.14348\/ref-25","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.ahj.2019.02.013","article-title":"Trends and predictors of recurrent acute coronary syndrome hospitalizations and unplanned revascularization after index acute myocardial infarction treated with percutaneous coronary intervention","volume":"212","author":"Yudi","year":"2019","journal-title":"American Heart Journal"}],"container-title":["PeerJ"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/peerj.com\/articles\/14348.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/14348.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/14348.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/14348.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T08:13:59Z","timestamp":1668500039000},"score":1,"resource":{"primary":{"URL":"https:\/\/peerj.com\/articles\/14348"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,15]]},"references-count":25,"alternative-id":["10.7717\/peerj.14348"],"URL":"https:\/\/doi.org\/10.7717\/peerj.14348","archive":["CLOCKSS","LOCKSS","Portico"],"relation":{},"ISSN":["2167-8359"],"issn-type":[{"value":"2167-8359","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,15]]},"article-number":"e14348"}}