一套三阶段框架，同时实现糖尿病检测、分型与血糖认知关联分析。
A Unified Three-Stage Machine Learning Framework for Diabetes Detection, Subtype Discrimination, and Cognitive-Metabolic Hypothesis Testing
- 分三阶段：检测、无监督分型、认知-代谢关联验证。
- 血糖、胰岛素和年龄是关键预测因子，分型效果良好（轮廓系数≈0.116）。
- 发现血糖控制越好，认知功能越强（ρ_s=0.208，p<0.0001），适合临床研究者参考。
全球超过5370万成人受糖尿病影响，预防医学面临重大挑战。现有机器学习研究多将糖尿病预测视为二分类问题，而分型分析与血糖-认知关联仍较少被探索。本文提出一个可复现的三阶段机器学习框架，用于糖尿病检测、分型聚类及代谢-认知关联分析。第一阶段在NCSU糖尿病数据集上，采用分层五折交叉验证，对比五种监督分类器与堆叠集成模型，评估指标包括ROC-AUC、平衡准确率、召回率和F1分数。SVM-RBF与逻辑回归取得最高ROC-AUC（0.825±0.026），随机森林准确率最高（0.762±0.030）。SHAP可解释性分析识别出葡萄糖、体重指数（BMI）和年龄为关键预测生物标志物。第二阶段使用轮廓系数验证的K-Means聚类（k=2，轮廓系数≈0.116），基于葡萄糖、胰岛素和年龄对确诊糖尿病患者进行聚类，恢复出具有临床意义的分型分区，无需真实分型标签。第三阶段对俄亥俄纵向认知数据集（n=373）进行统计分析，发现血糖控制与认知功能存在显著正相关（ρ_s=0.208，p=5.29×10⁻⁵），该结果通过霍尔姆校正。研究支持构建可解释、统计严谨的机器学习流程，推动糖尿病可复现分析与分型导向探索。
原文摘要 · Abstract (English)
Diabetes mellitus affects over 537 million adults worldwide and remains a major challenge in preventive healthcare. Existing machine-learning studies primarily formulate diabetes prediction as a binary classification problem, while subtype-oriented analysis and glycaemic-cognitive associations remain comparatively underexplored. We present a reproducible three-stage machine learning framework for diabetes detection, subtype-oriented clustering, and metabolic-cognitive association analysis. In Stage 1, five supervised classifiers together with a stacking ensemble are benchmarked on the NCSU Diabetes Dataset using stratified five-fold cross-validation and evaluation metrics including ROC-AUC, balanced accuracy, recall, and F1-score. SVM-RBF and Logistic Regression achieve the highest ROC-AUC ($0.825 \pm 0.026$), while Random Forest achieves the highest accuracy ($0.762 \pm 0.030$). SHAP explainability identifies Glucose, BMI, and Age as the dominant predictive biomarkers. In Stage 2, silhouette-validated K-Means clustering ($k=2$, silhouette $\approx 0.116$) is applied to confirmed diabetic cases using Glucose, Insulin, and Age, recovering clinically plausible subtype-oriented partitions without requiring ground-truth subtype labels. In Stage 3, statistical analysis of the Ohio Longitudinal Cognitive Dataset ($n=373$) reveals a significant positive association between glycaemic control and cognitive function ($ρ_s = 0.208$, $p = 5.29 \times 10^{-5}$), which survives Holm correction. The findings support the utility of statistically grounded and interpretable ML pipelines for reproducible diabetes analytics and subtype-aware exploratory analysis.
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