biostudies-literatureUnknown14(1)Liu Z<h4>Background</h4>Endovascular therapy (EVT) was the standard treatment for acute ischemic stroke with large vessel occlusion. Prognosis after EVT is always a major concern. Here, we aimed to explore a predictive model for patients after EVT.<h4>Method</h4>A total of 156 patients were retrospectively enrolled. The primary outcome was functional dependence (defined as a 90-day modified Rankin Scale score ≤ 2). Least absolute shrinkage and selection operator and univariate logistic regression were used to select predictive factors. Various machine learning algorithms, including multivariate logistic regression, linear discriminant analysis, support vector machine, <i>k</i>-nearest neighbors, and decision tree algorithms, were applied to construct prognostic models.<h4>Result</h4>Six predictTranslational neuroscience20220324https://www.ebi.ac.uk/biostudies/studies/S-EPMC10685342biostudies-literaturePredicting functional outcome in acute ischemic stroke patients after endovascular treatment by machine learning.PMC10685342Cai QLiu YLiu ZXie YOuyang KHou BZhang RfalsePredicting functional outcome in acute ischemic stroke patients after endovascular treatment by machine learning.<h4>Background</h4>Endovascular therapy (EVT) was the standard treatment for acute ischemic stroke with large vessel occlusion. Prognosis after EVT is always a major concern. Here, we aimed to explore a predictive model for patients after EVT.<h4>Method</h4>A total of 156 patients were retrospectively enrolled. The primary outcome was functional dependence (defined as a 90-day modified Rankin Scale score ≤ 2). Least absolute shrinkage and selection operator and univariate logistic regression were used to select predictive factors. Various machine learning algorithms, including multivariate logistic regression, linear discriminant analysis, support vector machine, <i>k</i>-nearest neighbors, and decision tree algorithms, were applied to construct prognostic models.<h4>Result</h4>Six predict2023-01-01T00:00:00Z2023 Jan2025-04-26T00:21:21.792Z2025-04-06T09:39:28.45ZS-EPMC106853423803515010.1515/tnsci-2022-0324