Supplementary Material for: Development of fall risk classification models for community-dwelling older adults using latent class analysis and machine learning

Introduction: To identify fall risk groups among community-dwelling older adults in South Korea and build a classification model by investigating risk-associated factors.
Methods: This cross-sectional study analyzed data of 9,231 older adults from the the 2020 Elderly Survey. We used latent class analysis to identify fall risk groups based on fall indicators. Thereafter, classification models were developed with these identified groups as outcome variables.
Results: Latent class analysis results indicated that a three-class model was more interpretable and fit the data better than other models. Among the models, the XGBoost algorithm displayed superior performance (accuracy = 0.70, precision = 0.69, recall = 0.70, F1-score = 0.68). Key variables associated with fall risk groups included self-rated health, cognitive function, recent healthcare use, and assistance needed in instrumental activities of daily living.
Conclusion: The study adopted a preventive approach by differentiating among low-, moderate-, and high-fall-risk groups, thus providing valuable insights for healthcare professionals. Identifying these risk factors can support the development of customized fall prevention programs for older adults.