Identifying key fall risk factors in older adults within a multifactorial intervention using feature selection methods - Teles, Raul R.; de Melo, Mariana L.; Gramani-Say, Karina; Ansai, Juliana H.; Pelicioni, Paulo H. S.; Ponciano, Jean R. (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais Estendidos do XLI Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd_estendido.2026.249576, 2026)
Approximately 27% of older adults experience fall-related injuries each year. To reduce falls, multifactorial interventions assess individual risk factors and develop targeted care plans. While these interventions may reduce falls, their success depends on the accurate identification of risk factors and the implementation of appropriate plans. In this paper, we applied feature selection methods to identify relevant variables for predicting falls in three distinct moments of an intervention, achieving precisions of 0.6, 0.6 and 0.57 and recalls 0.71 0.75 and 1.0. Relevant factors found include mobility, anxiety and depression, fear of falling, home hazards, physical activity, and income. We expect these results to assist domain experts by enabling data-driven decision-making.