融合加速度与非加速度数据，用贝叶斯模型提升老人跌倒风险预测准确率。
Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors
- 结合加速度计与非加速度数据训练机器学习模型
- 贝叶斯岭回归达最高精度（MSE=0.6746，R²=0.9941）
- 适合关注老年跌倒预防的临床与健康科技研究者
本研究基于146名老年人的加速度计、非加速度计及两者结合的数据，对比多种机器学习模型在跌倒风险预测中的表现。结果显示，融合两类数据的模型性能最优，其中贝叶斯岭回归表现最佳，均方误差为0.6746，决定系数达0.9941。年龄、共病等非加速度计变量对预测具有关键作用。研究支持采用集成数据与贝叶斯方法提升跌倒风险评估能力，为预防策略提供依据。
原文摘要 · Abstract (English)
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.
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