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Twenty-three clinical features were obtained from medical records and questionnaires before discharge. Early post-stroke fatigue was assessed using the Fatigue Severity Scale. The dataset was randomly divided into a training group (70%) and an internal validation group (30%), applied oversampling, 10-fold cross-validation, and grid search to optimize the hyperparameter. Feature selection using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Sixteen ML algorithms were performed to predict early post-stroke fatigue in this study. Accuracy, precision, recall,\n                      <jats:italic>F<\/jats:italic>\n                      1 score, area under the receiver operating characteristic curve (AUC), and brier score were used to evaluate the models performance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among the 16 ML algorithms, the Bagging model was the optimal model for predicting early post-stroke fatigue in patients with stroke (AUC\u2009=\u20090.8479, accuracy\u2009=\u20090.7518, precision\u2009=\u20090.5741, recall\u2009=\u20090.7209,\n                      <jats:italic>F<\/jats:italic>\n                      1 score\u2009=\u20090.6392, brier score\u2009=\u20090.1490). The feature selection based on LASSO revealed that risk factors for early post-stroke fatigue in patients with stroke included anxiety, sleep, social support, family care, pain, depression, neural-functional defect, quit\/no drinking, balance function, type of stroke, sex, heart disease, smoking, and hemiplegia.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In this study, the Bagging model proved to be effective in predicting early post-stroke fatigue.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15459683251329893","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T19:04:51Z","timestamp":1742843091000},"page":"433-444","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Machine Learning-Based Model for Prediction of Early Post-Stroke Fatigue in Patients With Stroke: A Longitudinal Study"],"prefix":"10.1177","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2398-7317","authenticated-orcid":false,"given":"Yu","family":"Wu","sequence":"first","affiliation":[{"name":"School of Nursing and Rehabilitation, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China"},{"name":"University of Health and Rehabilitation Sciences, Qingdao, Shandong Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Depeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Electronics and Information, Qingdao University, Qingdao, Shandong Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lovel","family":"Fornah","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Nursing and Rehabilitation, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Rehabilitation, Capital Medical University, Beijing, China"},{"name":"China Rehabilitation Research Center, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shicai","family":"Wu","sequence":"additional","affiliation":[{"name":"China Rehabilitation Research Center, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(21)00252-0"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(18)30500-3"},{"key":"e_1_3_3_4_2","unstructured":"National Stroke Center. 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