Composite socio-environmental risk score for cardiovascular assessment: An explainable machine learning approach.

ABSTRACT:

Background

Cardiovascular disease (CVD) is the leading global cause of death, with socio-environmental factors significantly influencing morbidity and mortality. Understanding these factors is essential for improving risk assessments and interventions.

Objective

To develop and evaluate the predictive power of a composite socio-environmental (SE) cardiovascular risk score in forecasting major adverse cardiovascular events (MACE) among patients, considering both traditional and novel socio-environmental risk factors.

Methods

A Survival Random Forest (RSF) model was used to create a composite socio-environmental (SE) cardiovascular risk score using 22 census-tract level variables from 62,438 patients in the CLARIFY registry undergoing coronary artery calcium (CAC) scorin

SUBMITTER: Chen Z 

PROVIDER: S-EPMC11976227 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature