Background
Predicting the risk of osteoporotic fractures is vital for prevention. Traditional methods such as the Fracture Risk Assessment Tool (FRAX) model use clinical factors. This study examined the predictive power of the FRAX score and machine-learning algorithms trained on FRAX parameters.
Methods
We analyzed the data of 2,147 female participants from the Ansan cohort study. The FRAX parameters employed in this study included age, sex (female), height and weight, current smoking status, excessive alcohol consumption (>3 units/d of alcohol), and diagnosis of rheumatoid arthritis. Osteoporotic fracture was defined as one or more fractures of the hip, spine, or wrist during a 10-year observation period. Machine-learning algorithms, such as gradient boosting, random forest, decision tree, and logistic regression, were employed to predict osteoporotic fractures with a 70:30 training-to-test set ratio. We evaluated the area under the receiver operating characteristic curve (AUROC) scores to assess and compare the performance of these algorithms with the FRAX score.
Results
Of the 2,147 participants, 3.5% experienced osteoporotic fractures. Those with fractures were older, shorter in height, and had a higher prevalence of rheumatoid arthritis, as well as higher FRAX scores. The AUROC for the FRAX was 0.617. The machine-learning algorithms showed AUROC values of 0.662, 0.652, 0.648, and 0.637 for gradient boosting, logistic regression, decision tree, and random forest, respectively.
Conclusion
This study highlighted the immense potential of machine-learning algorithms to improve osteoporotic fracture risk prediction in women when complete FRAX parameter information is unavailable.

Citations

• Machine learning is changing osteoporosis detection: an integrative review
  Yuji Zhang, Ming Ma, Xingchun Huang, Jinmin Liu, Cong Tian, Zhenkun Duan, Hongyin Fu, Lei Huang, Bin Geng
  Osteoporosis International.2025; 36(8): 1313.     CrossRef
• Artificial intelligence in nutrition and ageing research – A primer on the benefits
  Pol Grootswagers, Tijl Grootswagers
  Maturitas.2025; 200: 108662.     CrossRef
• Assessing the Risk of Osteoporotic Fracture Recurrence Using CT-based Radiomics and Machine Learning
  Xiaoyang Zheng, Caihong Zhu, Rui Zhang, Hongyu Sun
  Current Problems in Surgery.2025; : 101876.     CrossRef
• AI-driven Technologies for Wrist Fracture Prediction: A Narrative Review of Emerging Approaches
  Stefania Briano, Maria Cesarina May, Giacomo Demontis, Giulia Pachera, Vittoria Mazzola, Federico Vitali, Alessandra Galuppi, Emanuela Dapelo, Andrea Zanirato, Matteo Formica
  Journal of Wrist Surgery.2025;[Epub]     CrossRef
• Interpretable machine learning model for low bone density screening in older adults using demographic and anthropometric data: findings from 2005 to 2020 NHANES
  Weiyan Huang, Qimou Pan, Jiewei Peng, Yufeng Wu, Dawei Gao
  BMC Medical Informatics and Decision Making.2025;[Epub]     CrossRef
• Clinical Applicability of Machine Learning in Family Medicine
  Jungun Lee
  Korean Journal of Family Medicine.2024; 45(3): 123.     CrossRef
• Integrating Machine Learning for Personalized Fracture Risk Assessment: A Multimodal Approach
  Sheikh Mohd Saleem, Shah Sumaya Jan
  Korean Journal of Family Medicine.2024; 45(6): 356.     CrossRef