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

arXiv: https://arxiv.org/abs/2508.03756 · HTML: https://mlsanity.com/paper/2508.03756/

## Key facts

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- First posted on arXiv 4 Aug 2025.

## Abstract (arXiv)

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.