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Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data.
German Cuaya-Simbro1, Alberto-I Perez-Sanpablo2, Eduardo-F Morales3
1Instituto Tecnológico Superior del Oriente del Estado de Hidalgo (ITESA), Carretera Apan-Tepeapulco Km 3.5, Colonia Las Peñitas, Apan Hidalgo, Mexico.
Journal of Healthcare Engineering
|September 20, 2021
Summary
Computational models can predict falls in older women with osteoporosis. Random Forest and IBk (K-Nearest Neighbors) classifiers, using oversampling methods, show promise for identifying fall risks based on balance parameters.
Area of Science:
- Gerontology
- Biomedical Engineering
- Data Science
Background:
- Falls are a major cause of injury in older adults, especially those with osteoporosis.
- Identifying individuals at high risk for falls is crucial for preventive interventions.
Purpose of the Study:
- To evaluate computational methods for predicting falls in older women with osteoporosis.
- To analyze the effectiveness of balance parameters in fall prediction models.
Main Methods:
- A prospective study followed 126 community-dwelling older women with osteoporosis for 2.5 years, recording falls.
- Balance parameters were measured using posturography (eyes open and closed).
- Machine learning models (IBk/KNN, Random Forest) with oversampling and feature selection were applied to predict falls.
Main Results:
- Random Forest classifier with oversampling demonstrated good predictive performance (sensitivity >0.71, specificity >0.18, PPV >0.74, NPV >0.66).
- The feature selection for minority class (FSMC) method identified novel balance parameters.
- IBk (KNN) classifier using oversampling and all variables achieved the highest performance (sensitivity >0.81, specificity >0.19, PPV =0.97, NPV =0.66).
Conclusions:
- Machine learning, particularly Random Forest and IBk (KNN) with oversampling, can effectively predict falls in older women with osteoporosis.
- Intelligent computing methods can uncover significant balance parameters often overlooked by traditional analysis.
- These models offer potential for developing predictive clinical tests to mitigate fall-related injuries.
