{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T22:55:23Z","timestamp":1767999323815,"version":"3.49.0"},"reference-count":35,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2019,9,30]],"date-time":"2019-09-30T00:00:00Z","timestamp":1569801600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2017R1D1A1A02018718"],"award-info":[{"award-number":["2017R1D1A1A02018718"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Informatics J"],"published-print":{"date-parts":[[2020,6]]},"abstract":"<jats:p> Cardiovascular disease is the leading cause of death worldwide so, early prediction and diagnosis of cardiovascular disease is essential for patients affected by this fatal disease. The goal of this article is to propose a machine learning\u2013based 1-year mortality prediction model after discharge in clinical patients with acute coronary syndrome. We used the Korea Acute Myocardial Infarction Registry data set, a cardiovascular disease database registered in 52 hospitals in Korea for 1 November 2005\u201330 January 2008 and selected 10,813 subjects with 1-year follow-up traceability. The ranges of hyperparameters to find the best prediction model were selected from four different machine learning models. Then, we generated each machine learning\u2013based mortality prediction model with hyperparameters completed the range fitness via grid search using training data and was evaluated by fourfold stratified cross-validation. The best prediction model with the highest performance was found, and its hyperparameters were extracted. Finally, we compared the performance of machine learning\u2013based mortality prediction models with GRACE in area under the receiver operating characteristic curve, precision, recall, accuracy, and F-score. The area under the receiver operating characteristic curve in applied machine learning algorithms was averagely improved up to 0.08 than in GRACE, and their major prognostic factors were different. This implementation would be beneficial for prediction and early detection of major adverse cardiovascular events in acute coronary syndrome patients. <\/jats:p>","DOI":"10.1177\/1460458219871780","type":"journal-article","created":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T03:10:22Z","timestamp":1569899422000},"page":"1289-1304","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":53,"title":["A machine learning\u2013based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome"],"prefix":"10.1177","volume":"26","author":[{"given":"Syed Waseem Abbas","family":"Sherazi","sequence":"first","affiliation":[{"name":"Chungbuk National University, South Korea"}]},{"given":"Yu Jun","family":"Jeong","sequence":"additional","affiliation":[{"name":"Chungbuk National University, South Korea"}]},{"given":"Moon Hyun","family":"Jae","sequence":"additional","affiliation":[{"name":"Chungbuk National University, South Korea"}]},{"given":"Jang-Whan","family":"Bae","sequence":"additional","affiliation":[{"name":"Chungbuk National University, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5526-946X","authenticated-orcid":false,"given":"Jong Yun","family":"Lee","sequence":"additional","affiliation":[{"name":"Chungbuk National University, South Korea"}]}],"member":"179","published-online":{"date-parts":[[2019,9,30]]},"reference":[{"key":"bibr1-1460458219871780","unstructured":"World Health Organization. The top 10 causes of death, https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/the-top-10-causes-of-death (2018, accessed 24 May 2018)."},{"issue":"16","key":"bibr2-1460458219871780","first-page":"2020","volume":"126","author":"Thygesen K","year":"2012","journal-title":"J Am Coll Cardiol"},{"key":"bibr3-1460458219871780","doi-asserted-by":"crossref","unstructured":"Saqlain M, Hussain W, Saqib NA, et al. Identification of heart failure by using unstructured data of cardiac patients. In: International conference on parallel processing workshops, 2016, pp. 426\u2013431, https:\/\/ieeexplore.ieee.org\/document\/7576495","DOI":"10.1109\/ICPPW.2016.66"},{"key":"bibr4-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehr236"},{"key":"bibr5-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(13)61752-3"},{"key":"bibr6-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.107.699579"},{"key":"bibr7-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0088312"},{"key":"bibr8-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.327.7426.1267"},{"key":"bibr9-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.39609.449676.25"},{"key":"bibr10-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.j2099"},{"key":"bibr11-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.38985.646481.55"},{"key":"bibr12-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1016\/j.ahj.2009.06.010"},{"key":"bibr13-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjcard.2016.07.029"},{"key":"bibr14-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1186\/s12933-015-0274-4"},{"key":"bibr15-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"issue":"4","key":"bibr16-1460458219871780","first-page":"221","volume":"4","author":"Mokashi AR","year":"2016","journal-title":"International J Innovat Res Elect Electr Instrument Contr Eng"},{"issue":"6","key":"bibr17-1460458219871780","first-page":"386","volume":"5","author":"Subha V","year":"2015","journal-title":"Int J Comp Sci Comm Networks"},{"issue":"4","key":"bibr18-1460458219871780","first-page":"18","volume":"1","author":"Ahmed A","year":"2012","journal-title":"Int J Innovat Tech Explor Eng"},{"key":"bibr19-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa0807646"},{"key":"bibr20-1460458219871780","doi-asserted-by":"crossref","unstructured":"Wolterink JM, Leiner T, Viergever MA, et al. Dilated convolutional neural networks for cardiovascular MR segmentation in congenital heart disease. In: RAMBO 2016, HVSMR 2016: reconstruction, segmentation, and analysis of medical images, 2016, pp. 95\u2013102, https:\/\/link.springer.com\/chapter\/10.1007\/978-3-319-52280-7_9","DOI":"10.1007\/978-3-319-52280-7_9"},{"key":"bibr21-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1080\/20476965.2018.1547348"},{"key":"bibr22-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1002\/hpm.2771"},{"key":"bibr23-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2017-018628"},{"key":"bibr24-1460458219871780","doi-asserted-by":"publisher","DOI":"10.4258\/hir.2013.19.2.121"},{"key":"bibr25-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"bibr26-1460458219871780","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2013.00021"},{"key":"bibr27-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1201\/9781439891148"},{"key":"bibr28-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2017.11.003"},{"key":"bibr29-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjcard.2010.11.018"},{"key":"bibr30-1460458219871780","unstructured":"Mayo Medical Laboratories. https:\/\/www.mayomedicallaboratories.com\/"},{"key":"bibr31-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1038\/srep45637"},{"key":"bibr32-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1080\/09603123.2011.605876"},{"key":"bibr33-1460458219871780","unstructured":"PASW Statistics. http:\/\/www.spss.com.hk\/statistics\/ (accessed 1 July 2018)."},{"key":"bibr34-1460458219871780","unstructured":"H2O.ai. https:\/\/www.h2o.ai\/ (accessed 1 July 2018)."},{"key":"bibr35-1460458219871780","doi-asserted-by":"publisher","DOI":"10.1109\/TSM.2019.2904306"}],"container-title":["Health Informatics Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1460458219871780","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/1460458219871780","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1460458219871780","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T19:19:22Z","timestamp":1740770362000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/1460458219871780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,30]]},"references-count":35,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["10.1177\/1460458219871780"],"URL":"https:\/\/doi.org\/10.1177\/1460458219871780","relation":{},"ISSN":["1460-4582","1741-2811"],"issn-type":[{"value":"1460-4582","type":"print"},{"value":"1741-2811","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,30]]}}}