Machine learning prediction for menopause women with low bone mass: a multicenter and retrospective study.

Researchers developed a computer-based prediction tool to identify menopausal women at risk for low bone mass and osteoporosis. The study analyzed data from 3,738 menopausal women at a hospital and 1,008 women from community settings between 2014 and 2022. Using machine learning technology, scientists created a model that uses 11 different health factors to predict which women are most likely to have weak bones.

The prediction tool proved highly accurate, correctly identifying women with low bone mass about 91% of the time in both hospital and community settings. The XGboost machine learning model performed particularly well. This represents a significant advancement in early detection, as catching bone loss early creates opportunities to prevent fractures before they occur.

While this study focused on computer-based prediction rather than treatment approaches, the findings are relevant to acupuncture practitioners and patients. Many women seeking acupuncture care are postmenopausal and may be at risk for osteoporosis without knowing it. Acupuncture practitioners who work with this population can use awareness of these risk factors to encourage appropriate screening and integrate bone health into their holistic treatment planning. Traditional Chinese medicine approaches, including acupuncture, are sometimes used alongside conventional osteoporosis management to support overall wellness during menopause.

The study emphasizes the importance of identifying at-risk women early so they can receive preventive interventions. For women considering complementary approaches like acupuncture for menopausal symptoms, this research highlights the value of comprehensive health assessment. If you're interested in acupuncture care during menopause, seek a qualified, licensed acupuncturist who can work collaboratively with your primary care provider.

This multicenter retrospective study developed machine learning models to predict low bone mass (LBM) in postmenopausal women. The study included 3,738 menopausal women from a hospital setting (internal validation) and 1,008 from community settings (external validation), collected between December 2014 and February 2022. Researchers employed LASSO and elastic net methods for variable selection, ultimately identifying 11 clinical risk factors for model development.

Multiple ML algorithms and logistic regression were tested, with the XGboost model demonstrating superior performance. The optimal model achieved an AUC of 0.918 in internal validation and 0.910 in external validation, indicating excellent discriminatory ability for predicting LBM risk.

Clinical implications: This highly accurate prediction tool enables early identification of postmenopausal women at elevated osteoporosis risk, facilitating timely preventive interventions before fractures occur. For acupuncture practitioners treating menopausal patients, awareness of these risk factors supports comprehensive patient assessment and appropriate referral for bone density screening when indicated. The model's strong performance across both hospital and community populations suggests broad clinical applicability in primary care and integrative medicine settings.