Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors
This work addresses fall risk assessment for older adults, but it is incremental as it applies existing methods to a specific dataset.
This study tackled fall risk prediction in older adults by comparing machine learning models on accelerometric and non-accelerometric data, finding that combined data with Bayesian Ridge Regression achieved high accuracy (MSE = 0.6746, R2 = 0.9941).
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.