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Due to the asymptomatic progression of most CAS patients, early identification is challenging for clinicians, and it may trigger ischemic stroke. Recently, machine learning has shown a strong ability to classify data and a potential for prediction in the medical field. The combined use of machine learning and the electronic health records of patients could provide clinicians with a more convenient and precise method to identify asymptomatic CAS.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Retrospective cohort study using routine clinical data of medical check-up subjects from April 19, 2010 to November 15, 2019. Six machine learning models (logistic regression [LR], random forest [RF], decision tree [DT], eXtreme Gradient Boosting [XGB], Gaussian Na\u00efve Bayes [GNB], and K-Nearest Neighbour [KNN]) were used to predict asymptomatic CAS and compared their predictability in terms of the area under the receiver operating characteristic curve (AUCROC), accuracy (ACC), and F1 score (F1).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of the 18,441 subjects, 6553 were diagnosed with asymptomatic CAS. Compared to DT (AUCROC 0.628, ACC 65.4%, and F1 52.5%), the other five models improved prediction: KNN\u2009+\u20097.6% (0.704, 68.8%, and 50.9%, respectively), GNB\u2009+\u200912.5% (0.753, 67.0%, and 46.8%, respectively), XGB\u2009+\u200916.0% (0.788, 73.4%, and 55.7%, respectively), RF\u2009+\u200916.6% (0.794, 74.5%, and 56.8%, respectively) and LR\u2009+\u200918.1% (0.809, 74.7%, and 59.9%, respectively). The highest achieving model, LR predicted 1045\/1966 cases (sensitivity 53.2%) and 3088\/3566 non-cases (specificity 86.6%). A tenfold cross-validation scheme further verified the predictive ability of the LR.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Among machine learning models, LR showed optimal performance in predicting asymptomatic CAS. Our findings set the stage for an early automatic alarming system, allowing a more precise allocation of CAS prevention measures to individuals probably to benefit most.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-021-01480-3","type":"journal-article","created":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T00:02:32Z","timestamp":1617580952000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["The prediction of asymptomatic carotid atherosclerosis with electronic health records: a comparative study of six machine learning models"],"prefix":"10.1186","volume":"21","author":[{"given":"Jiaxin","family":"Fan","sequence":"first","affiliation":[]},{"given":"Mengying","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Shusen","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Jinming","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Qingling","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Shuang","family":"Du","sequence":"additional","affiliation":[]},{"given":"Huiyang","family":"Qu","sequence":"additional","affiliation":[]},{"given":"Yuxuan","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Shuyin","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Meijuan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xi","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shuqin","family":"Zhan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,5]]},"reference":[{"issue":"2","key":"1480_CR1","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1161\/STROKEAHA.112.673129","volume":"44","author":"G Sirimarco","year":"2013","unstructured":"Sirimarco G, Amarenco P, Labreuche J, Touboul PJ, Alberts M, Goto S, Rother J, Mas JL, Bhatt DL, Steg PG, et al. 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