Machine learning approaches to predict hip fracture incidence: insights from the CHARLS dataset.

ABSTRACT:

Background

Hip fractures are a major health concern in the older adults, severely impacting patients' quality of life and straining healthcare systems. With China's aging population, their incidence is projected to increase. Thus, developing effective prediction models to identify high-risk individuals is essential for prevention.

Objective

The aim of this study was to develop and validate a reliable and accurate machine learning-based predictive model for hip fracture incidence to improve the prediction of the risk of hip fracture in community residents.

Methods

Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS), encompassing 21,095 individuals aged 45 years and older, of whom 616 reported hip fractures. Baseline data from these parti

SUBMITTER: Li Y 

PROVIDER: S-EPMC12834802 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature