用问卷数据预测老人四年后的体能评分，精度接近1分误差。
ML-based Short Physical Performance Battery future score prediction based on questionnaire data
- 用机器学习模型分析问卷数据预测体能衰退趋势。
- 最优模型XGBoost四年后预测误差仅0.79分。
- 精简特征后模型仍保持高精度，适合临床筛查使用。
有效延缓老年人体能衰退需在早期症状出现时即刻干预。本文研究基于问卷数据预测四年后短时体能评估量表（SPPB）得分的可行性。测试了随机森林、XGBoost、线性回归、密集神经网络和TabNet等机器学习算法。其中XGBoost表现最佳，平均绝对误差为0.79分。基于Shapley值分析，筛选出10至20个关键特征并重新训练XGBoost，平均绝对误差为0.82分。
原文摘要 · Abstract (English)
Effective slowing down of older adults\' physical capacity deterioration requires intervention as soon as the first symptoms surface. In this paper, we analyze the possibility of predicting the Short Physical Performance Battery (SPPB) score at a four-year horizon based on questionnaire data. The ML algorithms tested included Random Forest, XGBoost, Linear Regression, dense and TabNet neural networks. The best results were achieved for the XGBoost (mean absolute error of 0.79 points). Based on the Shapley values analysis, we selected smaller subsets of features (from 10 to 20) and retrained the XGBoost regressor, achieving a mean absolute error of 0.82.
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