Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort.

ABSTRACT:

Objective

Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk prediction models, such as the Framingham Cardiovascular Risk Score and the Systematic Coronary Risk Evaluation, often fall short due to their reliance on classical statistical methods and limited variable scope.

Materials and methods

This study harnessed machine learning (ML) techniques to develop and validate a comprehensive CVD risk prediction model using data from the UK Biobank cohort. Our approach incorporated genetic scores, clinical parameters and lifestyle factors to create separate models for overall CVD, cerebrovascular diseases, thrombotic diseases and other cardiovascular

SUBMITTER: Yang Q 

PROVIDER: S-EPMC12927374 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature