{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T16:40:03Z","timestamp":1790959203262,"version":"4.1.0"},"reference-count":40,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T00:00:00Z","timestamp":1692662400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Diabetes Invest"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Aims\/Introduction<\/jats:title><jats:p>Clinical guidelines for the management of individuals with type\u20092 diabetes mellitus endorse the systematic assessment of atherosclerotic cardiovascular disease risk for early interventions. In this study, we aimed to develop machine learning models to predict 3\u2010year atherosclerotic cardiovascular disease risk in Chinese type\u20092 diabetes mellitus patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>Clinical records of 4,722 individuals with type\u20092 diabetes mellitus admitted to 94 hospitals were used. The features included demographic information, disease histories, laboratory tests and physical examinations. Logistic regression, support vector machine, gradient boosting decision tree, random forest and adaptive boosting were applied for model construction. The performance of these models was evaluated using the area under the receiver operating characteristic curve. Additionally, we applied SHapley Additive exPlanation values to explain the prediction model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>All five models achieved good performance in both internal and external test sets (area under the receiver operating characteristic curve &gt;0.8). Random forest showed the highest discrimination ability, with sensitivity and specificity being 0.838 and 0.814, respectively. The SHapley Additive exPlanation analyses showed that previous history of diabetic peripheral vascular disease, older populations and longer diabetes duration were the three most influential predictors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The prediction models offer opportunities to personalize treatment and maximize the benefits of these medical interventions.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jdi.14069","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T09:20:45Z","timestamp":1692696045000},"page":"1289-1302","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Machine learning for the prediction of atherosclerotic cardiovascular disease during 3\u2010year follow up in Chinese type\u20092 diabetes mellitus patients"],"prefix":"10.1111","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0399-7656","authenticated-orcid":false,"given":"Jinru","family":"Ding","sequence":"first","affiliation":[{"name":"Shanghai AI Laboratory  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingying","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Endocrinology and Metabolism Peking University People's Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huwei","family":"Shi","sequence":"additional","affiliation":[{"name":"Shanghai AI Laboratory  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiyao","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai AI Laboratory  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuqing","family":"Luo","sequence":"additional","affiliation":[{"name":"Shanghai AI Laboratory  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Yang","sequence":"additional","affiliation":[{"name":"SenseTime Inc.  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