Construction of a clinical prediction model for osteoporosis in asymptomatic elderly population based on machine learning algorithm.

ABSTRACT:

Background

Osteoporosis is a metabolic bone disease characterized by a decrease in the amount of bone per unit volume. It is highly prevalent and has a harsh impact on patients' lives. The development of accurate predictive models for osteoporosis is beneficial in helping physicians improve the accuracy of clinical diagnosis and provide a high-quality treatment experience for older adults.

Method

In this study, a robust and accurate prediction model for osteoporosis was developed and validated based on machine learning and SHAP techniques. We validated the model using ROC, calibration, and DCA curves. The data in this paper were obtained from elderly participants in several communities in Beijing from June 2021 to May 2022, including 161 (27.6%) males and 423 (72.4%) females

SUBMITTER: Wang J 

PROVIDER: S-EPMC12463994 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature