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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-03-01
|
| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251331752 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849388097495629824 |
|---|---|
| author | Ana González-Castro José Alberto Benítez-Andrades Rubén González-González Camino Prada-García Raquel Leirós-Rodríguez |
| author_facet | Ana González-Castro José Alberto Benítez-Andrades Rubén González-González Camino Prada-García Raquel Leirós-Rodríguez |
| author_sort | Ana González-Castro |
| collection | DOAJ |
| description | 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 identify key contributing variable. Methods We applied random forest, XGBoost, AdaBoost, LightGBM, support vector regression (SVR), decision trees, and Bayesian ridge regression to a dataset of 146 older adults. Models were trained using accelerometric data (movement patterns) and non-accelerometric data (demographic and clinical variables). Performance was evaluated based on mean squared error (MSE) and coefficient of determination ( R 2 ), to assess how combining multiple data types influences prediction accuracy. Results Models trained on combined accelerometric and non-accelerometric data consistently outperformed those based on single data types. Bayesian ridge regression achieved the highest accuracy (MSE = 0.6746, R 2 = 0.9941), demonstrating superior performance compared to decision trees (MSE = 0.1907, R 2 = 0.8991) and SVR (MSE = 1.5243, R 2 = − 2.2532). Non-accelerometric factors, including age and comorbidities, significantly contributed to fall risk prediction. Conclusions Integrating accelerometric and non-accelerometric data improves fall risk prediction accuracy in older adults. Bayesian ridge regression trained on combined datasets provides superior predictive power compared to traditional models. These findings highlight the importance of multi-source data fusion for effective fall prevention strategies. Future work should validate these models in larger, more diverse populations to enhance clinical applicability. |
| format | Article |
| id | doaj-art-80ae4a9cecd34f86b149e2484378cfc7 |
| institution | Kabale University |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-80ae4a9cecd34f86b149e2484378cfc72025-08-20T03:42:25ZengSAGE PublishingDigital Health2055-20762025-03-011110.1177/20552076251331752Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factorsAna González-Castro0José Alberto Benítez-Andrades1Rubén González-González2Camino Prada-García3Raquel Leirós-Rodríguez4 Nursing and Physical Therapy Department, Universidad de León, Ponferrada, Spain SALBIS Research Group, Department of Electric, Systems and Automatics Engineering, Universidad de León, León, Spain Department of Electric, Systems and Automatics Engineering, Escuela de Ingenierías Industrial, Informática y Aeroespacial, Universidad de León, León, Spain Dermatology Service, Complejo Asistencial Universitario de León, León, Spain SALBIS Research Group, Nursing and Physical Therapy Department, Universidad de León, Ponferrada, SpainObjectives 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 identify key contributing variable. Methods We applied random forest, XGBoost, AdaBoost, LightGBM, support vector regression (SVR), decision trees, and Bayesian ridge regression to a dataset of 146 older adults. Models were trained using accelerometric data (movement patterns) and non-accelerometric data (demographic and clinical variables). Performance was evaluated based on mean squared error (MSE) and coefficient of determination ( R 2 ), to assess how combining multiple data types influences prediction accuracy. Results Models trained on combined accelerometric and non-accelerometric data consistently outperformed those based on single data types. Bayesian ridge regression achieved the highest accuracy (MSE = 0.6746, R 2 = 0.9941), demonstrating superior performance compared to decision trees (MSE = 0.1907, R 2 = 0.8991) and SVR (MSE = 1.5243, R 2 = − 2.2532). Non-accelerometric factors, including age and comorbidities, significantly contributed to fall risk prediction. Conclusions Integrating accelerometric and non-accelerometric data improves fall risk prediction accuracy in older adults. Bayesian ridge regression trained on combined datasets provides superior predictive power compared to traditional models. These findings highlight the importance of multi-source data fusion for effective fall prevention strategies. Future work should validate these models in larger, more diverse populations to enhance clinical applicability.https://doi.org/10.1177/20552076251331752 |
| spellingShingle | Ana González-Castro José Alberto Benítez-Andrades Rubén González-González Camino Prada-García Raquel Leirós-Rodríguez Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors Digital Health |
| title | Predicting fall risk in older adults: A machine learning comparison
of accelerometric and
non-accelerometric factors |
| title_full | Predicting fall risk in older adults: A machine learning comparison
of accelerometric and
non-accelerometric factors |
| title_fullStr | Predicting fall risk in older adults: A machine learning comparison
of accelerometric and
non-accelerometric factors |
| title_full_unstemmed | Predicting fall risk in older adults: A machine learning comparison
of accelerometric and
non-accelerometric factors |
| title_short | Predicting fall risk in older adults: A machine learning comparison
of accelerometric and
non-accelerometric factors |
| title_sort | predicting fall risk in older adults a machine learning comparison of accelerometric and non accelerometric factors |
| url | https://doi.org/10.1177/20552076251331752 |
| work_keys_str_mv | AT anagonzalezcastro predictingfallriskinolderadultsamachinelearningcomparisonofaccelerometricandnonaccelerometricfactors AT josealbertobenitezandrades predictingfallriskinolderadultsamachinelearningcomparisonofaccelerometricandnonaccelerometricfactors AT rubengonzalezgonzalez predictingfallriskinolderadultsamachinelearningcomparisonofaccelerometricandnonaccelerometricfactors AT caminopradagarcia predictingfallriskinolderadultsamachinelearningcomparisonofaccelerometricandnonaccelerometricfactors AT raquelleirosrodriguez predictingfallriskinolderadultsamachinelearningcomparisonofaccelerometricandnonaccelerometricfactors |