Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912521937485824 |
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| author | González-Castro, Ana Benítez-Andrades, José Alberto González-González, Rubén Prada-García, Camino Leirós-Rodríguez, Raquel |
| author_facet | González-Castro, Ana Benítez-Andrades, José Alberto González-González, Rubén Prada-García, Camino Leirós-Rodríguez, Raquel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03756 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors González-Castro, Ana Benítez-Andrades, José Alberto González-González, Rubén Prada-García, Camino Leirós-Rodríguez, Raquel Applications Machine Learning 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. |
| title | Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors |
| topic | Applications Machine Learning |
| url | https://arxiv.org/abs/2508.03756 |