Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults.

ABSTRACT:

Objective

To compare Cox models, machine learning (ML), and ensemble models combining both approaches, for prediction of stroke risk in a prospective study of Chinese adults.

Materials and methods

We evaluated models for stroke risk at varying intervals of follow-up (<9 years, 0-3 years, 3-6 years, 6-9 years) in 503 842 adults without prior history of stroke recruited from 10 areas in China in 2004-2008. Inputs included sociodemographic factors, diet, medical history, physical activity, and physical measurements. We compared discrimination and calibration of Cox regression, logistic regression, support vector machines, random survival forests, gradient boosted trees (GBT), and multilayer perceptrons, benchmarking performance against the 2017 Framingham Stroke Risk Profile. W

SUBMITTER: Chun M 

PROVIDER: S-EPMC8324240 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature