{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T19:21:03Z","timestamp":1781119263213,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T00:00:00Z","timestamp":1715817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Background: In patients with embolic stroke of undetermined source (ESUS), occult atrial fibrillation (AF) has been implicated as a key source of cardioembolism. However, only a minority acquire implantable cardiac loop recorders (ILRs) to detect occult paroxysmal AF, partly due to financial cost and procedural inconvenience. Without the initiation of appropriate anticoagulation, these patients are at risk of increased ischemic stroke recurrence. Hence, cost-effective and accurate methods of predicting AF in ESUS patients are highly sought after. Objective: We aimed to incorporate clinical and echocardiography data into machine learning (ML) algorithms for AF prediction on ILRs in ESUS. Methods: This was a single-center cohort study that included 157 consecutive patients diagnosed with ESUS from October 2014 to October 2017 who had ILR evaluation. We developed four ML models, with hyperparameters tuned, to predict AF detection on an ILR. Results: The median age of the cohort was 67 (IQR 59\u201374) years old and the median monitoring duration was 1051 (IQR 478\u20131287) days. Of the 157 patients, 32 (20.4%) had occult AF detected on the ILR. Support vector machine predicted for AF with a 95% confidence interval area under the receiver operating characteristic curve (AUC) of 0.736\u20130.737, multilayer perceptron with an AUC of 0.697\u20130.708, XGBoost with an AUC of 0.697\u20130.697, and random forest with an AUC of 0.663\u20130.674. ML feature importance found that age, HDL-C, and admitting heart rate were important non-echocardiography variables, while peak mitral A-wave velocity and left atrial volume were important echocardiography parameters aiding this prediction. Conclusion: Machine learning modeling incorporating clinical and echocardiographic variables predicted AF in ESUS patients with moderate accuracy.<\/jats:p>","DOI":"10.3390\/jpm14050534","type":"journal-article","created":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T06:44:31Z","timestamp":1715841871000},"page":"534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Machine Learning Modeling to Predict Atrial Fibrillation Detection in Embolic Stroke of Undetermined Source Patients"],"prefix":"10.3390","volume":"14","author":[{"given":"Chua","family":"Ming","sequence":"first","affiliation":[{"name":"Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geraldine J. W.","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, Faculty of Science, National University of Singapore, Singapore 117546, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao Hao","family":"Teo","sequence":"additional","affiliation":[{"name":"Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1150-9174","authenticated-orcid":false,"given":"Yao Neng","family":"Teo","sequence":"additional","affiliation":[{"name":"Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emma M. S.","family":"Toh","sequence":"additional","affiliation":[{"name":"Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tony Y. W.","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Cardiology, National University Heart Centre, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chloe Yitian","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayan","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hock Luen","family":"Teoh","sequence":"additional","affiliation":[{"name":"Division of Neurology, Department of Medicine, National University Hospital, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7856-0620","authenticated-orcid":false,"given":"Swee-Chong","family":"Seow","sequence":"additional","affiliation":[{"name":"Department of Cardiology, National University Heart Centre, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4249-0402","authenticated-orcid":false,"given":"Leonard L. L.","family":"Yeo","sequence":"additional","affiliation":[{"name":"Division of Neurology, Department of Medicine, National University Hospital, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2764-2869","authenticated-orcid":false,"given":"Ching-Hui","family":"Sia","sequence":"additional","affiliation":[{"name":"Department of Cardiology, National University Heart Centre, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7566-1626","authenticated-orcid":false,"given":"Gregory Y. H.","family":"Lip","sequence":"additional","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool L14 3PE, UK"},{"name":"Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, 9220 Aalborg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3262-0207","authenticated-orcid":false,"given":"Mehul","family":"Motani","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore"},{"name":"N.1 Institute for Health, National University of Singapore, Singapore 117456, Singapore"},{"name":"Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore"},{"name":"Institute of Data Science, National University of Singapore, Singapore 117602, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin YQ","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore"},{"name":"Division of Neurology, Department of Medicine, National University Hospital, Singapore 119074, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/S1474-4422(21)00252-0","article-title":"Global, regional, and national burden of stroke and its risk factors, 1990\u20132019: A systematic analysis for the Global Burden of Disease Study 2019","volume":"20","author":"Feigin","year":"2021","journal-title":"Lancet Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1161\/STROKEAHA.116.016414","article-title":"Embolic Stroke of Undetermined Source: A Systematic Review and Clinical Update","volume":"48","author":"Hart","year":"2017","journal-title":"Stroke"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/S1474-4422(13)70310-7","article-title":"Embolic strokes of undetermined source: The case for a new clinical construct","volume":"13","author":"Hart","year":"2014","journal-title":"Lancet Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e012858","DOI":"10.1161\/JAHA.119.012858","article-title":"Prevalence and Overlap of Potential Embolic Sources in Patients with Embolic Stroke of Undetermined Source","volume":"8","author":"Ntaios","year":"2019","journal-title":"J. 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