Predicting the future risk and outcomes of severe heart failure and coronary artery disease with machine learning in the UK Biobank Cohort.

ABSTRACT:

Background

In order to seriously impact the global burden of heart failure (HF) and coronary artery disease (CAD), identifying at-risk individuals as early as possible is vital. Risk calculator tools in wide clinical use today are informed by traditional statistical methods that have historically yielded only modest prediction accuracy.

Methods

This study uses machine learning algorithms to generate predictions models for the development and progression of severe HF and CAD. Participants (~485,000 followed in the UK Biobank over 7 years) were stratified by cardiac status at the time of enrollment (asymptomatic, high-risk and affected); separate prediction models were built for each stratum. Participants were split between a training set (80%) and holdout dataset (20%), all p

SUBMITTER: Taha K 

PROVIDER: S-EPMC12422514 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature