A simple and user-friendly machine learning model to detect osteoporosis in health examination populations in Southern Taiwan.

ABSTRACT:

Background

Osteoporosis is a growing public health concern in aging populations such as Taiwan, where limited utilization of dual-energy X-ray absorptiometry (DXA) often leads to underdiagnosis and even delayed treatment. Therefore, we leveraged machine learning (ML) and aimed to develop a simple and easily accessible model that effectively identifies individuals at high risk of osteoporosis.

Methods

This retrospective analysis enrolled 5510 men aged ≥50 years and 4720 postmenopausal women who underwent DXA at the Kaohsiung Veterans General Hospital, with another cohort of 610 men and 523 women for validation. We developed separate models for men and women using decision trees, random forests, support vector machines, k-nearest neighbors, extreme gradient boosting, and artif

SUBMITTER: Huang WC 

PROVIDER: S-EPMC11783436 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature