Abstract: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 ($\rho_s = 0.208$, $p = 5.29 \times 10^{-5}$), 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.
| Comments: | 10 Pages |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.13464 [cs.LG] |
| (or arXiv:2605.13464v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13464 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Vishal Pandey [view email]
[v1]
Wed, 13 May 2026 12:53:39 UTC (454 KB)