融合多源数据提升糖尿病预测精度，模型误差低于5%。
Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction
- 通过整合2011-2021年多源数据构建特征集
- 新模型EBMBag+在城市糖尿病预测中误差最低，MAPE仅4.01%
- 适合公共卫生规划与精准干预决策参考
糖尿病是一种以高血糖为特征的慢性代谢疾病，可导致心脏病、肾衰竭和神经损伤等并发症。准确的州级预测对医疗资源规划和靶向干预至关重要，但许多情况下分析所需数据不完整。本研究通过数据工程整合2011至2021年糖尿病相关数据集，构建全面特征集，并提出一种增强型袋装集成回归模型（EBMBag+），用于美国城市糖尿病患病率的时间序列预测。对比SVMReg、BDTree、LSBoost、NN、LSTM和ERMBag等基线模型，实验结果表明EBMBag+表现最佳，实现平均绝对误差（MAE）0.41，均方根误差（RMSE）0.53，平均绝对百分比误差（MAPE）4.01%，决定系数（R²）达0.9。
原文摘要 · Abstract (English)
Diabetes is a chronic metabolic disease characterized by elevated blood glucose levels, leading to complications like heart disease, kidney failure, and nerve damage. Accurate state-level predictions are vital for effective healthcare planning and targeted interventions, but in many cases, data for necessary analyses are incomplete. This study begins with a data engineering process to integrate diabetes-related datasets from 2011 to 2021 to create a comprehensive feature set. We then introduce an enhanced bagging ensemble regression model (EBMBag+) for time series forecasting to predict diabetes prevalence across U.S. cities. Several baseline models, including SVMReg, BDTree, LSBoost, NN, LSTM, and ERMBag, were evaluated for comparison with our EBMBag+ algorithm. The experimental results demonstrate that EBMBag+ achieved the best performance, with an MAE of 0.41, RMSE of 0.53, MAPE of 4.01, and an R2 of 0.9.
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