A Multicohort Machine Learning Framework to Predict Mortality in Elderly Patients With Heart Disease: Insights From HARLS, SHARE, and HRS.

ABSTRACT:

Background

Elderly patients with heart disease face elevated mortality risk, yet predictive models specifically tailored for this population across different global regions remain limited. Current mortality prediction tools often lack cross-cultural validation and interpretability, hindering their clinical application in diverse healthcare settings.

Methods

We developed and validated machine learning models for predicting mortality in elderly heart disease patients using data from three major aging cohorts: the China Health and Retirement Longitudinal Study (CHARLS, n = 2130), the Survey of Health, Ageing and Retirement in Europe (SHARE, n = 10,928), and the Health and Retirement Study (HRS) from the United States (n = 4835). Boruta feature selection ide

SUBMITTER: Yang Z 

PROVIDER: S-EPMC12759112 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature