Machine learning techniques for independent gait recovery prediction in acute anterior circulation ischemic stroke.

ABSTRACT:

Objective

This study aimed to develop and validate a machine learning-based predictive model for gait recovery in patients with acute anterior circulation ischemic stroke.

Methods

Between May and November 2023, 237 patients with acute anterior circulation ischemic stroke were enrolled. Patients were randomly divided into training and validation sets at a 7:3 ratio. Thirty-one medical characteristics were collected, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to screen predictor variables. Predictive models were developed using the Random Survival Forest (RSF) and COX regression methods. The optimal model was identified based on C-index values. The SHapley Additive exPlanations (SHAP) method was employed to interpret the RSF model gl

SUBMITTER: Ma J 

PROVIDER: S-EPMC11786359 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature