Supplementary Material for: AI-Driven Fall Prediction Across Generations: Integrating Deep Learning and Machine Learning for Young, Middle-Aged, and Older Adults

Introduction Falls occur in all age groups and represent a significant public health concern. Previous studies have implemented artificial intelligence (AI), including machine learning (ML) and deep learning (DL) algorithms for fall risk prediction, but the comparative performance between models and the applicability for younger populations remains unclear. This study aims to develop and compare different ML/DL models and identify key predictive features across age groups. Methods We enrolled 1441 community-dwelling adults aged over 20 years in southern Taiwan and collected demographic, clinical, and physical performance data. Participants were categorized based on fall history. Five ML models (KNN, RF, GBDT, XGBoost, and CatBoost) and two DL (GRU, AGRU) models were trained and evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUROC). Feature importance was interpreted using SHapley Additive exPlanations (SHAP) values in the best-performing model. Age-stratified subgroup analyses were conducted for groups aged 20-45, 46-65, and >65 years. Results The AGRU model achieved the highest accuracy (91.39%) and AUROC (0.934) in the overall group and outperformed other models across all subgroups. Feature importance analysis revealed pulse rate, living alone, systolic blood pressure, 5-times Sit-to-Stand test, and sex as major predictors of falls in the overall group. The top five predicting factors varied across age groups. Conclusion We developed a robust and interpretable DL model for identifying fall risk across different age groups. Age-specific risk factors highlight the need for tailored preventive strategies. External validation using an independent dataset demonstrated moderate generalizability. Larger and more diverse datasets for validation and integration of sequential or sensor-based data are essential for practical applications.