A Unified Three-Stage Machine Learning Framework for Diabetes Detection, Subtype Discrimination, and Cognitive-Metabolic Hypothesis Testing

• URL: http://arxiv.org/abs/2605.13464v1
• Date: Wed, 13 May 2026 12:53:39 GMT
• Title: A Unified Three-Stage Machine Learning Framework for Diabetes Detection, Subtype Discrimination, and Cognitive-Metabolic Hypothesis Testing
• Abstract summary: Diabetes affects over 537 million adults worldwide and remains a major challenge in preventive healthcare.<n>We present a reproducible three-stage machine learning framework for diabetes detection, subtype-oriented clustering, and metabolic-cognitive association analysis.
• Score: 0.5599792629509229
• License: http://creativecommons.org/licenses/by/4.0/
• 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 ($ρ_s = 0.208$, $p = 5.29 \times 10^{-5}$), 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.

Related papers

• Explainable Retinal Imaging for Prediction of Multi-Organ Dysfunction in Type 2 Diabetes [2.153295259565971]
  System-level abnormality indices were constructed to quantify organ-specific dysfunction.<n>Supervised machine learning models were trained to predict multi-system dysregulation.<n>Hyperglycaemia, renal impairment, dyslipidaemia, and inflammation were the dominant drivers of multi-system risk.
  arXiv  Detail & Related papers  (2026-05-24T07:31:57Z)
• Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction [0.5399800035598186]
  This study begins with a data engineering process to integrate diabetes-related datasets from 2011 to 2021.<n>We then introduce an enhanced bagging ensemble regression model (EBMBag+) for time series forecasting to predict diabetes prevalence across U.S. cities.<n>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.
  arXiv  Detail & Related papers  (2025-06-11T04:21:50Z)
• Insulin Resistance Prediction From Wearables and Routine Blood Biomarkers [44.735350554750916]
  We remotely recruited the largest dataset to date across the US to study insulin resistance.<n>Deep neural network models were developed to predict insulin resistance based on readily available digital and blood biomarkers.<n>Model showed 93% sensitivity and 95% adjusted specificity in obese and sedentary participants.
  arXiv  Detail & Related papers  (2025-04-30T16:10:20Z)
• Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation [46.36100528165335]
  Photoplethysmography and electrocardiography can potentially enable continuous blood pressure (BP) monitoring.<n>Yet accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors.<n>In this work, we investigate whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type.<n>Our approach achieves near state-of-the-art accuracy for diastolic BP and surpasses by 1.5x the accuracy of prior works for systolic BP.
  arXiv  Detail & Related papers  (2025-02-10T13:33:12Z)
• From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis [47.23780364438969]
  We present GluFormer, a generative foundation model for CGM data that learns nuanced glycemic patterns and translates them into predictive representations of metabolic health.<n>GluFormer generalizes to 19 external cohorts spanning different ethnicities and ages, 5 countries, 8 CGM devices, and diverse pathophysiological states.<n>In a longitudinal study of 580 adults with CGM data and 12-year follow-up, GluFormer identifies individuals at elevated risk of developing diabetes more effectively than blood HbA1C%.
  arXiv  Detail & Related papers  (2024-08-20T13:19:06Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Supervised Learning Models for Early Detection of Albuminuria Risk in Type-2 Diabetes Mellitus Patients [0.0]
  This study aimed to develop a supervised learning model to predict the risk of developing albuminuria in T2DM patients. It consisted of 10 attributes as features and 1 attribute as the target (albuminuria) It achieved accuracy and f1-score values as high as 0.74 and 0.75, respectively, making it suitable for screening purposes in predicting albuminuria in T2DM.
  arXiv  Detail & Related papers  (2023-09-28T08:41:12Z)
• Machine Learning-Based Diabetes Detection Using Photoplethysmography Signal Features [0.0]
  Diabetes is a prevalent chronic condition that compromises the health of millions of people worldwide. Here, we present an alternative method to overcome shortcomings based on non-invasive optical photoplethysmography for detecting diabetes. We classify non-Diabetic and Diabetic patients using the PPG signal and algorithms for training Logistic Regression and eXtreme Gradient Boosting. Our findings are within the same range reported in the literature, indicating that machine learning methods are promising for developing remote, non-invasive, and continuous measurement devices for detecting and preventing diabetes.
  arXiv  Detail & Related papers  (2023-08-02T14:10:03Z)
• Machine Learning based prediction of Glucose Levels in Type 1 Diabetes Patients with the use of Continuous Glucose Monitoring Data [0.0]
  Continuous Glucose Monitoring (CGM) devices offer detailed, non-intrusive and real time insights into a patient's blood glucose concentrations. Leveraging advanced Machine Learning (ML) Models as methods of prediction of future glucose levels, gives rise to substantial quality of life improvements.
  arXiv  Detail & Related papers  (2023-02-24T19:10:40Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers [5.746418369209802]
  This paper introduces the Diabetic Foot Ulcers dataset (DFUC 2021) for analysis of pathology, focusing on infection and ischaemia. The final release of DFUC 2021 consists of 15,683 DFU patches, with 5,955 training, 5,734 for testing and 3,994 unlabeled DFU patches. We benchmark the performance of five key backbones of deep learning, i.e. VGG16, ResNet101, InceptionV3, DenseNet121 and EfficientNet on DFUC 2021.
  arXiv  Detail & Related papers  (2021-04-07T11:38:57Z)
• A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability [76.64661091980531]
  People with diabetes are at risk of developing diabetic retinopathy (DR) Computer-aided DR diagnosis is a promising tool for early detection of DR and severity grading. This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists.
  arXiv  Detail & Related papers  (2020-08-22T07:48:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.