Development and validation of a stroke risk prediction model using regional healthcare big data and machine learning.

ABSTRACT:

Objectives

This study aimed to develop and validate a stroke risk prediction model based on machine learning (ML) and regional healthcare big data, and determine whether it may improve the prediction performance compared with the conventional Logistic Regression (LR) model.

Methods

This retrospective cohort study analyzed data from the CHinese Electronic health Records Research in Yinzhou (CHERRY) (2015-2021). We included adults aged 18-75 from the platform who had established records before 2015. Individuals with pre-existing stroke, key data absence, or excessive missingness (>30 %) were excluded. Data on demographic, clinical measures, lifestyle factors, comorbidities, and family history of stroke were collected. Variable selection was performed in two stages: an initial

SUBMITTER: Duan Y 

PROVIDER: S-EPMC12684759 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature