Explainable machine learning for identification of risk factors in high fall-risk older adults in the community

Falls are the leading cause of injury-related deaths and hospitalizations among the elderly, highlighting the need for effective falls prevention strategies. These consequences can significantly impact the well-being of the elderly and their families. The certain study aims to provide an explainable approach that could identify high-risk individuals and risk factors related to falls. Data were recorded from adults 55-90 years of any gender in the province of East Macedonia and Thrace in Greece. This study considered multidisciplinary data gathered from interview-administered questionnaires and physical performance tests among the elderly. A total of 208 variables were considered, with the main indicator for assessing falling status being a combination of the John Hopkins Fall Risk Assessment tool score and the number of falls during an exercise program. Using binary classification, the study aimed to predict high-risk fallers and non-fallers. Specifically, the first class consists of 29 subjects who were classified as high-risk fallers, while the second class consists of 41 subjects who were classified as non-fallers. The proposed approach combines a wrapper feature selection algorithm with three well-known machine learning (ML) models. Through this approach, a concise subset of only 8 risk factors was identified, achieving an accuracy rate of 95.24%. To further analyze the significance of these risk factors, the study used SHapley additive explanations (SHAP). Overall, the findings of this study have the potential to assist in the development of effective risk stratification strategies and the identification of risk profiles of each individual who falls, ultimately enabling appropriate interventions to be implemented.

C. Kokkotis, A. Kanavaki, E. Kouli, A. Gkrekidis, D. Menychtas, P. Manaveli, M. Michalopoulou, E. Douda, V. Gourgoulis, I. Smilios, G. Sirakoulis, N. Aggelousis