Machine learning and Mendelian randomization identify key lifestyle factors in coronary heart disease: A NHANES-Based study.

ABSTRACT:

Objective

This study aims to bridge the gap between predictive modeling and causal inference by utilizing lifestyle data from the National Health and Nutrition Examination Survey (NHANES) database to compare the predictive performance of multiple machine learning models for coronary heart disease (CHD). By incorporating Mendelian randomization, the study seeks to validate and identify the lifestyle variables with both predictive power and causal impact on CHD.

Methods

We extracted variables related to demographic characteristics and lifestyle from the NHANES database (2013-2018; n = 29,400). Recursive feature elimination (RFE) was employed to rank variable importance and determine the optimal feature subset. Subsequently, eight machine learning models-including Support Vecto

SUBMITTER: Cui YY 

PROVIDER: S-EPMC12637256 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature