Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors
Objectives Accurate prediction of fall risk in older adults is essential to prevent injuries and improve quality of life. This study evaluates the predictive performance of various machine learning models using accelerometric data, non-accelerometric data, aiming to improve predictive accuracy and i...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-03-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251331752 |
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