Prediction of Long-Term Stroke Recurrence Using Machine Learning Models.

ABSTRACT:

Background

The long-term risk of recurrent ischemic stroke, estimated to be between 17% and 30%, cannot be reliably assessed at an individual level. Our goal was to study whether machine-learning can be trained to predict stroke recurrence and identify key clinical variables and assess whether performance metrics can be optimized.

Methods

We used patient-level data from electronic health records, six interpretable algorithms (Logistic Regression, Extreme Gradient Boosting, Gradient Boosting Machine, Random Forest, Support Vector Machine, Decision Tree), four feature selection strategies, five prediction windows, and two sampling strategies to develop 288 models for up to 5-year stroke recurrence prediction. We further identified important clinical features and different opti

SUBMITTER: Abedi V 

PROVIDER: S-EPMC8003970 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature