A primary care-oriented framework for osteoporosis prediction: integrating multi-algorithm feature selection and machine learning model benchmarking.
- 1Health Management Center, the Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Abstract
Summary
Large-scale screening and primary care diagnosis of osteoporosis are challenging due to the limited availability of dual-energy X-ray absorptiometry (DXA). Therefore, predictive models for osteoporosis that do not rely on bone mineral density measurements are essential. Using routine health examination data and applying a consensus feature selection strategy, we constructed machine learning (ML)-based models and compared their performance across multiple dimensions.Purpose
To develop a robust, low-cost, and interpretable framework for osteoporosis prediction based on readily available information, which can be easily integrated into electronic health systems to inform DXA referral in resource-limited settings.Methods
Variables covered by the basic health examination package were obtained from participants who underwent health examinations between January 2024 and January 2025. We implemented a strategy in which only features selected by at least four out of the seven feature selection methods were retained. Six ML algorithms were employed to develop models, and their prediction performances were compared across discrimination, goodness-of-fit, and clinical utility.Results
A total of 2,260 participants were included, with an osteoporosis prevalence of 7.1%. Eight features: age, sex, body mass index, alkaline phosphatase, hematocrit, high-density lipoprotein, urea, and platelet count, were selected for model development. In the validation set, the random forest and artificial neural network (ANN) models demonstrated strongest screening performance, with AUCs of 0.895 (95% CI 0.867-0.923) and 0.890 (95% CI 0.862-0.919), respectively. The ANN model additionally exhibited superior calibration. Shapley additive explanations were applied to interpret the ANN model for osteoporosis prediction.Conclusion
This study presents an easily integrable model for osteoporosis prediction based on routine health examination data. The ANN model provides robust prediction, offering a potential tool for pre-DXA triage and early identification of high-risk individuals in primary care and resource-limited settings.
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BibTeX
@article{guan2026primary,
title = {{A primary care-oriented framework for osteoporosis prediction: integrating multi-algorithm feature selection and machine learning model benchmarking.}},
author = {Xiaofang Guan and Xiaoqi Wang and Wenjing Zhang and Juanjuan Li and Zhili Hou and Zhaoting Hu and Pei Sun},
journal = {Frontiers in endocrinology},
year = {2026},
month = oct,
doi = {10.3389/fendo.2026.1917386},
url = {https://europepmc.org/article/MED/42898138},
note = {CC BY},
}
RIS
TY - JOUR
TI - A primary care-oriented framework for osteoporosis prediction: integrating multi-algorithm feature selection and machine learning model benchmarking.
AU - Xiaofang Guan
AU - Xiaoqi Wang
AU - Wenjing Zhang
AU - Juanjuan Li
AU - Zhili Hou
AU - Zhaoting Hu
AU - Pei Sun
PY - 2026
DA - 2026/10/05
JO - Frontiers in endocrinology
DO - 10.3389/fendo.2026.1917386
UR - https://europepmc.org/article/MED/42898138
AB - Summary Large-scale screening and primary care diagnosis of osteoporosis are challenging due to the limited availability of dual-energy X-ray absorptiometry (DXA). Therefore, predictive models for osteoporosis that do not rely on bone mineral density measurements are essential. Using routine health examination data and applying a consensus feature selection strategy, we constructed machine learning (ML)-based models and compared their performance across multiple dimensions.Purpose To develop a robust, low-cost, and interpretable framework for osteoporosis prediction based on readily available information, which can be easily integrated into electronic health systems to inform DXA referral in resource-limited settings.Methods Variables covered by the basic health examination package were obtained from participants who underwent health examinations between January 2024 and January 2025. We implemented a strategy in which only features selected by at least four out of the seven feature selection methods were retained. Six ML algorithms were employed to develop models, and their prediction performances were compared across discrimination, goodness-of-fit, and clinical utility.Results A total of 2,260 participants were included, with an osteoporosis prevalence of 7.1%. Eight features: age, sex, body mass index, alkaline phosphatase, hematocrit, high-density lipoprotein, urea, and platelet count, were selected for model development. In the validation set, the random forest and artificial neural network (ANN) models demonstrated strongest screening performance, with AUCs of 0.895 (95% CI 0.867-0.923) and 0.890 (95% CI 0.862-0.919), respectively. The ANN model additionally exhibited superior calibration. Shapley additive explanations were applied to interpret the ANN model for osteoporosis prediction.Conclusion This study presents an easily integrable model for osteoporosis prediction based on routine health examination data. The ANN model provides robust prediction, offering a potential tool for pre-DXA triage and early identification of high-risk individuals in primary care and resource-limited settings.
SN - 1664-2392
ER -