Predicting functional outcome in acute ischemic stroke patients after endovascular treatment by machine learning.

ABSTRACT:

Background

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.

Method

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, k-nearest neighbors, and decision tree algorithms, were applied to construct prognostic models.

Result

Six predict

SUBMITTER: Liu Z 

PROVIDER: S-EPMC10685342 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature