This paper is in the following e-collection/theme issue:

Reviews in Medical Informatics (119) Artificial Intelligence (2297) Diabetes Reviews and Scoping Studies (42) Tools, Programs and Algorithms (402) Machine Learning (2199) Decision Support for Health Professionals (1612) Clinical Information and Decision Making (2037)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47833, first published .
Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis

Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis

Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis

Authors of this article:

Kui Liu1 Author Orcid Image ;   Linyi Li1 Author Orcid Image ;   Yifei Ma1 Author Orcid Image ;   Jun Jiang1 Author Orcid Image ;   Zhenhua Liu1 Author Orcid Image ;   Zichen Ye1 Author Orcid Image ;   Shuang Liu1 Author Orcid Image ;   Chen Pu1 Author Orcid Image ;   Changsheng Chen2 Author Orcid Image ;   Yi Wan1 Author Orcid Image
Article Authors Cited by (29) Tweetations (3) Metrics

Journals

  1. Abdelbaky I, Ahmed M, Taha M. Machine learning classification approaches for prediction of effective diabetes drugs. Egyptian Informatics Journal 2025;31:100786 View
  2. Schnell O, Ziegler R. The Promise of Hypoglycemia Risk Prediction. Journal of Diabetes Science and Technology 2024;18(5):1061 View
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  4. Lee J, Yun H, Jeon H. The Convergence of Polymer Science and Predictive Modeling for Noninvasive Glucose Monitoring. Pharmaceutics 2025;17(11):1488 View
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  6. Kapoor Y, Hasija Y. Continuous glucose monitoring using machine learning models and IoT device data: A meta-analysis. Technology and Health Care 2025;33(1):577 View
  7. Hwang M, Rachim V, Yoo J, Lee Y, Park S. Generalized multi task learning framework for glucose forecasting and hypoglycemia detection using simulation to reality. npj Digital Medicine 2025;8(1) View
  8. Kladov D, Berikov V, Semenova J, Klimontov V. Machine Learning Algorithms Based on Time Series Pre-Clustering for Nocturnal Glucose Prediction in People with Type 1 Diabetes. Diagnostics 2024;14(21):2427 View
  9. Gaitán-Guerrero J, Luis López Ruiz J, Espinilla M, Martínez-Cruz C. Toward an Interpretable Continuous Glucose Monitoring Data Modeling. IEEE Internet of Things Journal 2024;11(19):31080 View
  10. Ryu J, Ru J, Kang Y, Yang S. A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease. Scientific Reports 2025;15(1) View
  11. Saleh A, Hussien S. Accurate Numerical Prediction Strategy (NPS) based on feature weighting and soft computing techniques. Knowledge-Based Systems 2025;325:113918 View
  12. Shen S, Qi W, Li S, Zeng J, Liu X, Zhu X, Dong C, Wang B, Xu Q, Cao S. Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study. DIGITAL HEALTH 2025;11 View
  13. Kannenberg S, Voggel J, Thieme N, Witt O, Pethahn K, Schütt M, Sina C, Freckmann G, Schröder T. Unlocking Potential: Personalized Lifestyle Therapy for Type 2 Diabetes Through a Predictive Algorithm-Driven Digital Therapeutic. Journal of Diabetes Science and Technology 2026;20(1):113 View
  14. Du Y, Yang P, Liu Y, Deng C, Li X. Artificial intelligence in chronic disease self-management: current applications and future directions. Frontiers in Public Health 2025;13 View
  15. Gao Y, Wang C, Shen J, Wang Z, Liu Y, Chai Y. Systematic review and network meta-analysis of machine learning algorithms in sepsis prediction. Expert Systems with Applications 2024;245:122982 View
  16. Haleem M, Katsarou D, Georga E, Dafoulas G, Bargiota A, Lopez-Perez L, Rujas M, Fico G, Pecchia L, Fotiadis D, Caimi C, Tamporale C, Manea M, Bonferini C, Gaeta E, Sánchez G, Drympeta I, Votis K, Folkvord F, de Battle J. A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes management. Scientific Reports 2025;15(1) View
  17. Kumar V, Divekar A, Habbu S, Joshi S, Joshi A, Dalvi V. Non-invasive blood glucose estimation using a novel white-box model: An interpretable machine learning approach. Biomedical Signal Processing and Control 2025;105:107520 View
  18. Lim M, Chae H, Yoon J, Shin I. A deep learning framework for virtual continuous glucose monitoring and glucose prediction based on life-log data. Scientific Reports 2025;15(1) View
  19. Tominaga H, Hamaguchi M, Hamaguchi Y, Yashiki R, Yamaguchi A, Arai T, Yamazaki M, Kitagawa N, Hashimoto Y, Okada H, Fukui M. Prediction of Postprandial Blood Glucose Variability Using Machine Learning in Frequent Insulin Injection Therapy with a Simplified Carbohydrate Counting Model. Nutrients 2025;17(24):3832 View
  20. Masood S, Al-Bashrawi M, Khan M, Dwivedi Y. From data to diagnosis: a systematic review on AI-driven approaches to diabetes prediction. Artificial Intelligence Review 2026;59(3) View
  21. Tallon E, Williams D, Schweisberger C, Mullaney C, Lockee B, Ferro D, Vandervelden C, Barnes M, Sarteau A, Kahkoska A, Patton S, Mehta S, McDonough R, Lind M, D'Avolio L, Clements M. Toward a Clinically Actionable, Electronic Health Record–Based Machine Learning Model to Forecast 90-Day Change in Hemoglobin A1c in Youth With Type 1 Diabetes: Feasibility and Model Development Study. JMIR Diabetes 2025;10:e69142 View
  22. Ying Z, Li X, Chen Y. Artificial intelligence in glycemic management for diabetes: Applications, opportunities and challenges. Journal of Translational Internal Medicine 2025;13(4):314 View

Conference Proceedings

  1. Mohammed H, Oleiwi Jasim A, Mohammed Z, Sulaiman S, Mhmood S, Ahmed S. 2025 3rd International Conference on Cyber Resilience (ICCR). Insulin Dose Recommendation System for Type-1 Diabetes Patients Using Reinforcement Learning and Continuous Glucose Monitoring Data View
  2. Kozinets R, Semenova J, Berikov V, Klimontov V. 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). Deep Learning and Machine Learning Models for Predicting Day-Time Glucose in Different Ranges in Patients with Type 1 Diabetes View
  3. Nayaki A, Naganandhini V, Mai B, Elavarasi A. 2025 International Conference on Sustainable Communication Networks and Application (ICSCN). An Optuna-Tuned XGBoost Framework with SMOTE-ENN for Imbalanced Diabetes Data Classification View
  4. Upamanyu M, Chandan M, Amrutha H, Veena K, Upendra R, Karthik R. 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). Early Prediction of Type- II Diabetes Mellitus in Young Adults using LSTM View
  5. Ghimire S, Celik T, Gerdes M, Omlin C. 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS). Bayesian Neural Network for Uncertainty-Aware Blood Glucose Prediction for Type 1 Diabetes View
  6. Mégane J, Lourenço N, Hidalgo J, Machado P. Proceedings of the Genetic and Evolutionary Computation Conference. Contribution of Probabilistic Structured Grammatical Evolution to efficient exploration of the search space. A case study in glucose prediction View
  7. Mishra S, Dash S, Padhy S, Kumar N, Dash Y. 2024 7th International Conference on Contemporary Computing and Informatics (IC3I). Integrating Multi-Omics Data for Advanced Diabetes Prediction and Understanding View