Cardiovascular risk prediction and influencing predictors identification among Bangladeshi individuals using machine learning algorithms and association rule mining.

ABSTRACT:

Background

Cardiovascular disease (CVD) encompasses a group of disorders that affect the heart and blood vessels, making it one of the leading causes of death globally, including in Bangladesh. Applying predictive modeling for the early identification and detection of CVD holds significant promise for saving lives by enhancing prediction precision through machine learning algorithms. Therefore, this study aimed to predict high-risk individuals for CVD using machine learning algorithms and identify its influencing predictors by association mining rules among individuals in Bangladesh.

Materials and methods

This study utilized the most recent Bangladesh Demographic and Health Survey (BDHS) 2022 data, which encompassed 2,221 respondents. A Boruta-based feature selection method

SUBMITTER: Islam MM 

PROVIDER: S-EPMC12503297 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature