Open Access
Issue
ITM Web Conf.
Volume 82, 2026
International Conference on NextGen Engineering Technologies and Applications for Sustainable Development (ICNEXTS’25)
Article Number 03010
Number of page(s) 9
Section Information and Technology
DOI https://doi.org/10.1051/itmconf/20268203010
Published online 04 February 2026
  1. N. Nisha Nadhira Nazirun et al., “Prediction Models for Type 2 Diabetes Progression: A Systematic Review”, IEEE Access 12 161595-161619, doi: 10.1109/ACCESS.2024.3432118 (2024). [Google Scholar]
  2. P. G. Jacobs et al., “Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities”, IEEE 17 Reviews in Biomedical Engineering, 19-41, doi: 10.1109/RBME.2023.3331297(2024). [Google Scholar]
  3. N. F. Cleymans, M. Van De Casteele, J. Vandewalle, A. K. Desouter, F. K. Gorus and K. Barbé, “Analyzing Random Forest’s Predictive Capability for Type 1 Diabetes Progression”, IEEE Open Journal of Instrumentation and Measurement, 4, 1-11, Art no. 1000211, doi: 10.1109/OJIM.2025.3551837(2025). [Google Scholar]
  4. M. Panagiotou et al., “Personalized Insulin Adjustment With Reinforcement Learning: An In-Silico Validation for People With Diabetes on Intensive Insulin Treatment”, IEEE Access 13 148436-148455, doi: 10.1109/ACCESS.2025.3600738(2025). [Google Scholar]
  5. M. Saleh Al Reshan et al., “An Innovative Ensemble Deep Learning Clinical Decision Support System for Diabetes Prediction”, IEEE Access 12, 106193-106210, doi: 10.1109/ACCESS.2024.3436641(2024). [Google Scholar]
  6. R. Jain, N. Kumar Tripathi, M. Pant, C. Anutariya and C. Silpasuwanchai, “Investigating Gender and Age Variability in Diabetes Prediction: A Multi-Model Ensemble Learning Approach”, IEEE Access, 12, 71535-71554, doi: 10.1109/ACCESS.2024.3402350(2024). [Google Scholar]
  7. I. Shaheen, N. Javaid, N. Alrajeh, Y. Asim and S. Aslam, “Hi-Le and HiTCLe: Ensemble Learning Approaches for Early Diabetes Detection Using Deep Learning and Explainable Artificial Intelligence”, IEEE Access, 12, 66516-66538, doi: 10.1109/ACCESS.2024.3398198.(2024) [Google Scholar]
  8. S. Ahmed, M. S. Kaiser, M. Shahadat Hossain and K. Andersson, “A Comparative Analysis of LIME and SHAP Interpreters With Explainable ML-Based Diabetes Predictions”, IEEE Access 13, 37370-37388, doi: 10.1109/ACCESS.2024.3422319(2025). [Google Scholar]
  9. A. Site, J. Nurmi and E. S. Lohan, “Machine-Learning-Based Diabetes Prediction Using Multisensor Data”, IEEE Sensors Journal, 22, 28370-28377, doi: 10.1109/JSEN.2023.3319360(2023). [Google Scholar]
  10. S. Mam, D. Wichadakul and P. Vateekul, “Drug Repurposing for Type 2 Diabetes Using Combined Textual and Structural Graph Representation Based on Transformer”, IEEE Access 11, 65711-65724, 2023, doi: 10.1109/ACCESS.2023.3289863(2023). [Google Scholar]
  11. M. Sinsirimongkhon, S. Arwatchananukul and P. Temdee, “Multi-Class Classification Method with Feature Engineering for Predicting Hypertension with Diabetes”, Journal of Mobile Multimedia, 19, 799-821, doi: 10.13052/jmm1550-4646.1937 (2023). [Google Scholar]
  12. A. Alexiadis et al., “Next-Day Prediction of Hypoglycaemic Episodes Based on the Use of a Mobile App for Diabetes Self-Management”, IEEE Access 12 7469-7478, doi: 10.1109/ACCESS.2024.3350201 (2024). [Google Scholar]
  13. M. Aljaafari, S. E. El-Deep, A. A. Abohany and S. E. Sorour, “Integrating Innovation in Healthcare: The Evolution of “CURA’s” AI-Driven Virtual Wards for Enhanced Diabetes and Kidney Disease Monitoring”, IEEE Access 12, 126389-126414, doi: 10.1109/ACCESS.2024.3451369 (2024). [Google Scholar]
  14. G. Annuzzi et al., “Impact of Nutritional Factors in Blood Glucose Prediction in Type 1 Diabetes Through Machine Learning” , IEEE Access 11, 17104-17115, doi: 10.1109/ACCESS.2023.3244712 (2023). [Google Scholar]
  15. D. Parra, D. Joedicke, J. M. Velasco, G. Kronberger and J. I. Hidalgo, “Learning Difference Equations With Structured Grammatical Evolution for Postprandial Glycaemia Prediction”, IEEE Journal of Biomedical and Health Informatics, 28, 5, 3067-3078, , doi: 10.1109/JBHI.2024.3371108 (2024). [Google Scholar]
  16. G. Cappon, F. Prendin, A. Facchinetti, G. Sparacino and S. D. Favero, “Individualized Models for Glucose Prediction in Type 1 Diabetes: Comparing Black-Box Approaches to a Physiological White-Box One”, IEEE Transactions on Biomedical Engineering, 70,11, 3105-3115, doi: 10.1109/TBME.2023.3276193 (2023). [Google Scholar]
  17. J. M. Lee, R. Pop-Busui, J. M. Lee, J. Fleischer and J. Wiens, “Shortcomings in the Evaluation of Blood Glucose Forecasting” ,IEEE Transactions on Biomedical Engineering, 71, 12, 3424-3431, doi: 10.1109/TBME.2024.3424665 (2024). [Google Scholar]
  18. Z. Yu, W. Luo, R. Tse and G. Pau, “DMNet: A Personalized Risk Assessment Framework for Elderly People With Type 2 Diabetes”, IEEE Journal of Biomedical and Health Informatics 27, 1558-1568, doi: 10.1109/JBHI.2022.3233622 (2023). [Google Scholar]
  19. A. Al Bataineh, B. Vamsi and A. Al-Refai, “Predicting Diabetic Distress and Emotional Burden in Type-2 Diabetes Using Explainable AI”, IEEE Access 13, 109484-109502, doi: 10.1109/ACCESS.2025.3582191 (2025). [Google Scholar]
  20. A. Ali Linkon et al., “Evaluation of Feature Transformation and Machine Learning Models on Early Detection of Diabetes Mellitus”, IEEE Access 12, 165425-165440, doi: 10.1109/ACCESS.2024.3488743(2024). [Google Scholar]

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