Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.

ABSTRACT: Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction. A systematic review and random-effect meta-analysis were conducted according to preferred reporting items for systematic reviews and meta-analyses guidelines, assessing studies from 2010 to 2024. We retrieved 32 ML models and 26 conventional statistical models from 20 selected studies, focusing on perf

SUBMITTER: Liu T 

PROVIDER: S-EPMC11750195 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature