{"ID":2886752,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.03756","arxiv_id":"2508.03756","title":"Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors","abstract":"This study investigates fall risk prediction in older adults using various machine learning models trained on accelerometric, non-accelerometric, and combined data from 146 participants. Models combining both data types achieved superior performance, with Bayesian Ridge Regression showing the highest accuracy (MSE = 0.6746, R2 = 0.9941). Non-accelerometric variables, such as age and comorbidities, proved critical for prediction. Results support the use of integrated data and Bayesian approaches to enhance fall risk assessment and inform prevention strategies.","short_abstract":"This study investigates fall risk prediction in older adults using various machine learning models trained on accelerometric, non-accelerometric, and combined data from 146 participants. Models combining both data types achieved superior performance, with Bayesian Ridge Regression showing the highest accuracy (MSE = 0....","url_abs":"https://arxiv.org/abs/2508.03756","url_pdf":"https://arxiv.org/pdf/2508.03756v1","authors":"[\"Ana González-Castro\",\"José Alberto Benítez-Andrades\",\"Rubén González-González\",\"Camino Prada-García\",\"Raquel Leirós-Rodríguez\"]","published":"2025-08-04T07:33:56Z","proceeding":"stat.AP","tasks":"[\"stat.AP\",\"cs.LG\"]","methods":"[]","has_code":false}