A simple and user-friendly machine learning model to detect osteoporosis in health examination populations in Southern Taiwan.

Background: Osteoporosis is a growing public health concern in aging populations such as Taiwan, where limited utilization of dual-energy X-ray absorptiometry (DXA) often leads to underdiagnosis and even delayed treatment. Therefore, we leveraged machine learning (ML) and aimed to develop a simple and easily accessible model that effectively identifies individuals at high risk of osteoporosis. Methods: This retrospective analysis enrolled 5510 men aged ≥50 years and 4720 postmenopausal women who underwent DXA at the Kaohsiung Veterans General Hospital, with another cohort of 610 men and 523 women for validation. We developed separate models for men and women using decision trees, random forests, support vector machines, k-nearest neighbors, extreme gradient boosting, and artificial neural networks (ANNs) to predict osteoporosis. Furthermore, we compared each model with the traditional Osteoporosis Self-Assessment Tool for Asians (OSTA) model. Results: We identified age, height, weight, and BMI as variables for our prediction model and evaluated the model's performance using the area under the receiver operating characteristic curve (AUC). The ANN model significantly outperformed the OSTA model and all the other ML models for both men and women (AUC: 0.67 for men; 0.77 for women). The validation data for the ANN model showed similar AUCs for both men and women. Conclusion: This study developed ML models to help identify individuals at high risk of osteoporosis in postmenopausal women and men aged ≥50 years in southern Taiwan. Our ML models, especially the ANN model, surpassed the OSTA model and consistently performed well across different populations.