Do positive psychosocial factors contribute to the prediction of coronary artery disease? A UK Biobank-based machine learning approach.

ABSTRACT:

Aims

Most prediction models for coronary artery disease (CAD) compile biomedical and behavioural risk factors using linear multivariate models. This study explores the potential of integrating positive psychosocial factors (PPFs), including happiness, satisfaction with life, and social support, into conventional and machine learning-based CAD-prediction models.

Methods and results

We included UK Biobank (UKB) participants without CAD at baseline. First, we estimated associations of individual PPFs with subsequent acute myocardial infarction (AMI) and chronic ischaemic heart disease (CIHD) using logistic regression. Then, we compared the performances of logistic regression and eXtreme Gradient Boosting (XGBoost) prediction models when adding PPFs as predictors to the Framingh

SUBMITTER: Hefti R 

PROVIDER: S-EPMC12011491 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature