Assessing the Risk of Falls in Older Adults Living in the Community Using Machine Learning Models with Imputation (Student Abstract)

Abstract Falls are a major cause of injury and loss of independence among older adults, making prevention a critical priority for healthy aging. Early detection of fall risk through screening can enable timely interventions that reduce these adverse out-comes. Traditional clinical methods, such as using the history of falls and simple questionnaire-based screening, provide a quick and low-cost means of assessment but often have poor predictive accuracy and fail in presence of missing information. To support cost-effective screening and intervention, there is a need for tools that can accurately assess fall-risk in presence of missing information with better accuracy than cur-rent approaches. In this study, we developed a k-Nearest Neighbors (kNN) model that predicts whether an older adult will experience at least one fall within 12 months after baseline assessment, while simultaneously imputing missing data. Using data from 2,291 community-dwelling older adults in Singapore and 317 features spanning gait, cognition, physical activity, and comorbidities, our model achieved an AUC = 0.62 and F1 = 0.40, a significant improvement over the current clinical standard based solely on fall history (AUC ≈ 0.50). This model offers a more cost-effective screening tool for large-scale community deployment and highlights the feasibility of light-weight, imputation-aware models for practical fall-risk screening in aging populations.