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This study used machine learning techniques to develop and validate a new risk prediction model for new\u2010onset AF that incorporated the use electrocardiogram to diagnose AF, data from participants with a wide age range, and considered hypertension and measures of atrial stiffness. In Japan, Industrial Safety and Health Law requires employers to provide annual health check\u2010ups to their employees. This study included 13 410 individuals who underwent health check\u2010ups on at least four successive years between 2005 and 2015 (new\u2010onset AF, <jats:italic>n<\/jats:italic>\u00a0=\u00a0110; non\u2010AF, <jats:italic>n<\/jats:italic>\u00a0=\u00a013 300). Data were entered into a risk prediction model using machine learning methods (eXtreme Gradient Boosting and Shapley Additive Explanation values). Data were randomly split into a training set (80%) used for model construction and development, and a test set (20%) used to test performance of the derived model. The area under the receiver operator characteristic curve for the model in the test set was 0.789. The best predictor of new\u2010onset AF was age, followed by the cardio\u2010ankle vascular index, estimated glomerular filtration rate, sex, body mass index, uric acid, \u03b3\u2010glutamyl transpeptidase level, triglycerides, systolic blood pressure at cardio\u2010ankle vascular index measurement, and alanine aminotransferase level. This new model including arterial stiffness measure, developed with data from a general population using machine learning methods, could be used to identify at\u2010risk individuals and potentially facilitation the prevention of future AF development.<\/jats:p>","DOI":"10.1111\/jch.14848","type":"journal-article","created":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T14:44:35Z","timestamp":1717857875000},"page":"806-815","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Precise risk\u2010prediction model including arterial stiffness for new\u2010onset atrial fibrillation using machine learning techniques"],"prefix":"10.1111","volume":"26","author":[{"given":"Hiroshi","family":"Kanegae","sequence":"first","affiliation":[{"name":"Department of Medicine Division of Cardiovascular Medicine Jichi Medical University School of Medicine  Tochigi Japan"},{"name":"Genki Plaza Medical Center for Health Care  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kentaro","family":"Fujishiro","sequence":"additional","affiliation":[{"name":"Research and Development Division Japan Health Promotion Foundation  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyohei","family":"Fukatani","sequence":"additional","affiliation":[{"name":"Fukuda Denshi CO., LTD  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tetsuya","family":"Ito","sequence":"additional","affiliation":[{"name":"Fukuda Denshi CO., LTD  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8251-4480","authenticated-orcid":false,"given":"Kazuomi","family":"Kario","sequence":"additional","affiliation":[{"name":"Department of Medicine Division of Cardiovascular Medicine Jichi Medical University School of Medicine  Tochigi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,6,8]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.285.18.2370"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/joim.12114"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.2147\/CLEP.S47385"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.2188\/jea.15.194"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2018-021704"},{"key":"e_1_2_10_7_1","unstructured":"World Health Organization.World Population Prospects.2022. 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