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

Falls are a major cause of injury and loss of inde-pendence among older adults, making prevention a critical priority for healthy aging. Early detection of fall risk through screening can enable timely inter-ventions that reduce these adverse outcomes. Tradi-tional 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 pres-ence of missing information with better accuracy than current approaches. In this study, we devel-oped a k-Nearest Neighbors (KNN) model using da-ta from the 2,291 older Singaporeans and achieved an AUC of 0.62 and a F1 score of 0.40. This model is capable of simultaneously imputing missing fea-ture values while screening an individual for fall-risk.