Sci Rep. 2026 May 11. doi: 10.1038/s41598-026-50659-z. Online ahead of print.
ABSTRACT
Early diagnosis of postmenopausal osteoporosis provides an opportunity to detect and prevent fractures. This study uses machine learning (ML) techniques to enhance the predictive ability for low bone mass (LBM) risk. A retrospective cross-sectional study was performed, including 3,738 menopausal women from a hospital (the internal validation dataset) and 1,008 menopausal women from the community (the external validation dataset) between December 2014 and February 2022. The least absolute shrinkage and selection operation (LASSO) and elastic net methods are employed to screen the variables. ML algorithms and logistic regression are applied using clinical risk factors to develop a prediction model, and its effectiveness is subsequently evaluated. The optimal model is selected, and the concordance statistic is established for discrimination, comprising 11 variables. In predicting LBM, the model achieves an AUC of 0.918 in the internal validation dataset and 0.910 in the external validation dataset, with the XGboost model particularly noteworthy. This prediction model assists older women at elevated risk of osteoporosis, guiding decision-making for primary care providers to identify those needing preventive treatment.
PMID:42115688 | DOI:10.1038/s41598-026-50659-z
