Predictive models for secondary epilepsy in patients with acute ischemic stroke within one year.

ABSTRACT:

Background

Post-stroke epilepsy (PSE) is a critical complication that worsens both prognosis and quality of life in patients with ischemic stroke. An interpretable machine learning model was developed to predict PSE using medical records from four hospitals in Chongqing.

Methods

Medical records, imaging reports, and laboratory test results from 21,459 ischemic stroke patients were collected and analyzed. Univariable and multivariable statistical analyses identified key predictive factors. The dataset was split into a 70% training set and a 30% testing set. To address the class imbalance, the Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors was employed. Nine widely used machine learning algorithms were evaluated using relevant prediction metri

SUBMITTER: Liu J 

PROVIDER: S-EPMC11563573 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature