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arXiv · 2508.03756

Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors

Ana González-Castro·José Alberto Benítez-Andrades·Rubén González-González·Camino Prada-García·Raquel Leirós-Rodríguez
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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.

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Ana González-Castro, José Alberto Benítez-Andrades, Rubén González-González, Camino Prada-García, Raquel Leirós-Rodríguez. 2025-08-04. Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors. https://doi.org/10.1177/20552076251331752

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