Machine Learning Models for Personalized Treatment Recommendations in Diabetes Management
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Diabetes is a chronic and common metabolic ailment that needs constant monitoring and a change in therapy to prevent complications. The traditional methodology for treatment mostly depended on certain guidelines that may not be able to consider the individual differences in how each patient responds to treatment. With developments in artificial intelligence, machine learning has provided the advent of extremely powerful tools that can be applied to health care, for the provision of individualized clinical recommendations of treatment based on each patient's specific physiological and lifestyle factors.
Using machine learning techniques in managing diabetes, this article highlights, basically, the data-driven approaches likely to improve the treatment outcome. The article discusses key machine learning techniques, such as decision trees, neural networks, and reinforcement learning, which have been applied in the prediction of blood glucose levels, treatment recommendations, and proper adjustment of insulin dosages. Importance is also given to the sources from which data will be drawn, such as electronic health records (EHRs) and wearable devices, along with continuous glucose monitoring (CGM) equipment, in effecting beneficial build-up of robust machine learning models.
Challenges remain for ML treatment approaches with issues such as impenetrable model interpretability, privacy of data, and concerns over ethics. Unbiased algorithms and regulatory compliance are important aspects to pay attention to when deploying ML-enabled solutions in clinical settings. ML can also be carried into future technologies such as the Internet of Things (IoT) and federated learning, which can contribute to the accuracy and security of personalized treatment programs.
When it harnesses the power of ML in diabetes management, the practice will be able to form the efficiency of individualized treatments in such a way that they contribute to building patient adherence while minimizing complications, thus improving quality of life. This paper covers the entire spectrum of advancements and challenges brought about by ML concerning future research directions in personalized diabetes care and, thereby, is set to revolutionize every single aspect of disease management and healthcare delivery as a whole.
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1. Sun, X., Bee, Y. M., Lam, S. W., Liu, Z., Zhao, W., Chia, S. Y., ... & Xie, G. (2021). Effective treatment recommendations for type 2 diabetes management using reinforcement learning: treatment recommendation model development and validation. Journal of Medical Internet Research, 23(7), e27858.
2. Yogananda, C. G. B., Shah, B. R., Vejdani-Jahromi, M., Nalawade, S. S., Murugesan, G. K., Yu, F. F., ... & Maldjian, J. A. (2020). A fully automated deep learning network for brain tumor segmentation. Tomography, 6(2), 186.
3. Niha Malali; Sita Rama Praveen Madugula. “Robustness and Adversarial Resilience of Actuarial AI/ML Models in the Face of Evolving Threats.” Volume. 10 Issue.3, March-2025 International Journal of Innovative Science and Research Technology (IJISRT), 910-916, https://doi.org/10.38124/ijisrt/25mar1287
4. Malali, N., & Praveen Madugula, S. R. (2025). Robustness and Adversarial Resilience of Actuarial AI/ML Models in the Face of Evolving Threats. International Journal of Innovative Science and Research Technology, 10(3), 910-916.
5. Sola, R. P., Malali, N., & Madugula, P. (2025). Cloud Database Security: Integrating Deep Learning and Machine Learning for Threat Detection and Prevention: 0. Notion Press.
6. Nihar Malali. “AI Ethics in Financial Services: A Global Perspective.” Volume. 10 Issue.2, February-2025 International Journal of Innovative Science and Research Technology (IJISRT), 71-78, https://doi.org/10.5281/zenodo.14881349
7. Ruparel, H., Daftary, H., Singhai, V., & Kumar, P. The Impact of Generative AI on Cloud Data Security: A Systematic Study of Opportunities and Challenges.
8. Govindarajan, V., Sonani, R., & Patel, P. S. (2023). A Framework for Security-Aware Resource Management in Distributed Cloud Systems. Academia Nexus Journal, 2(2).
9. Govindarajan, V., Sonani, R., & Patel, P. S. (2020). Secure Performance Optimization in Multi-Tenant Cloud Environments. Annals of Applied Sciences, 1(1).
10. Vijay Krishnan, K., Viginesh, S., & Vijayraghavan, G. (2013). MACREE–A Modern Approach for Classification and Recognition of Earthquakes and Explosions. In Advances in Computing and Information Technology: Proceedings of the Second International Conference on Advances in Computing and Information Technology (ACITY) July 13-15, 2012, Chennai, India-Volume 2 (pp. 49-56). Springer Berlin Heidelberg.
11. Viginesh, S., Vijayraghavan, G., & Srinath, S. (2013). RAW: A Novel Reconfigurable Architecture Design Using Wireless for Future Generation Supercomputers. In Computer Networks & Communications (NetCom) Proceedings of the Fourth International Conference on Networks & Communications (pp. 845-853). Springer New York.
12. Hossain, F., Hasan, K., Amin, A., & Mahmud, S. (2024). Quantum Machine Learning for Enhanced Cybersecurity: Proposing a Hypothetical Framework for Next-Generation Security Solutions. Journal of Technologies Information and Communication, 4(1), 32222.
13. Nabil, A. R., Rayhan, R. U., Akther, M. N., & Tusher, M. (2024). Demystifying Edge AI: Unlocking the Potential of Artificial Intelligence at the Edge of the Network. International Journal of Innovative Science and Research Technology, 9(12).
14. Alam, M. A., Sohel, A., Biswas, A., Sifat, S. B. M., Nabil, A. R., Chowdhury, N., ... & Bappy, M. A. (2024). Privacy-preserving multi-class classification of acute lymphoblastic leukemia subtypes using federated learning.
15. Rao, D. D., Waoo, A. A., Singh, M. P., Pareek, P. K., Kamal, S., & Pandit, S. V. (2024). Strategizing IoT network layer security through advanced intrusion detection systems and AI-driven threat analysis. Full Length Article, 12(2), 195-195.
16. Rao, D. D. (2009, November). Multimedia based intelligent content networking for future internet. In 2009 Third UKSim European Symposium on Computer Modeling and Simulation (pp. 55-59). IEEE.
17. Venkatesh, R., Rao, D. D., Sangeetha, V., Subbalakshmi, C., Bala Dhandayuthapani, V., & Mekala, R. (2024). Enhancing Stability in Autonomous Control Systems Through Fuzzy Gain Scheduling (FGS) and Lyapunov Function Analysis. International Journal of Applied and Computational Mathematics, 10(4), 130.
18. Padmakala, S., Al-Farouni, M., Rao, D. D., Saritha, K., & Puneeth, R. P. (2024, August). Dynamic and Energy-Efficient Resource Allocation using Bat Optimization in 5G Cloud Radio Access Networks. In 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON) (pp. 1-4). IEEE.
19. Rao, D. D., Jain, A., Sharma, S., Pandit, S. V., & Pandey, R. (2024). Effectual energy optimization stratagems for wireless sensor network collections through fuzzy-based inadequate clustering. SN Computer Science, 5(8), 1-10.
20. Yadav, B., Rao, D. D., Mandiga, Y., Gill, N. S., Gulia, P., & Pareek, P. K. (2024). Systematic Analysis of threats, Machine Learning solutions and Challenges for Securing IoT environment. Journal of Cybersecurity & Information Management, 14(2).
21. Kumar, S., Joshitta, R. S. M., Rao, D. D., Masarath, S., & Waghmare, V. N. (2023, November). Storage Matched Systems for Single-Click Photo Recognition Using CNN. In 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI) (pp. 1-7). IEEE.
22. Ness, S. (2025). Securing Networks Against Adversarial Domain Name System Tunneling Attacks Using Hybrid Neural Networks. IEEE Access.
23. S. Ness, "Securing Networks Against Adversarial Domain Name System Tunneling Attacks Using Hybrid Neural Networks," in IEEE Access, vol. 13, pp. 46697-46709, 2025, doi: 10.1109/ACCESS.2025.3550853
24. Elhoseny, M., Rao, D. D., Veerasamy, B. D., Alduaiji, N., Shreyas, J., & Shukla, P. K. (2024). Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and Tolerance. International Journal of Computational Intelligence Systems, 17(1), 1-19.
25. Rao, D. D., Dhabliya, D., Dhore, A., Sharma, M., Mahat, S. S., & Shah, A. S. (2024, June). Content Delivery Models for Distributed and Cooperative Media Algorithms in Mobile Networks. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
26. Rao, D. D., Bala Dhandayuthapani, V., Subbalakshmi, C., Singh, M. P., Shukla, P. K., & Pandit, S. V. (2024). An efficient Analysis of the Fusion of Statistical-Centred Clustering and Machine Learning for WSN Energy Efficiency. Fusion: Practice & Applications, 15(2)
27. Nadeem, S. M., Rao, D. D., Arora, A., Dongre, Y. V., Giri, R. K., & Jaison, B. (2024, June). Design and Optimization of Adaptive Network Coding Algorithms for Wireless Networks. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
28. Rao, D. D., Waoo, A. A., & Singh, M. P. (2021). Breaking Down Barriers: Scalability and Performance Issues in Blockchain-Based Identity Platforms.
29. Zhang, J., Lv, Y., Hou, J., Zhang, C., Yua, X., Wang, Y., ... & Li, L. (2023). Machine learning for post-acute pancreatitis diabetes mellitus prediction and personalized treatment recommendations. Scientific Reports, 13(1), 4857.
30. Donsa, K., Spat, S., Beck, P., Pieber, T. R., & Holzinger, A. (2015). Towards personalization of diabetes therapy using computerized decision support and machine learning: some open problems and challenges. Smart Health: Open Problems and Future Challenges, 237-260.
31. Alian, S., Li, J., & Pandey, V. (2018). A personalized recommendation system to support diabetes self-management for American Indians. Ieee Access, 6, 73041-73051.
32. Afsaneh, E., Sharifdini, A., Ghazzaghi, H., & Ghobadi, M. Z. (2022). Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetology & Metabolic Syndrome, 14(1), 196.
33. Bertsimas, D., Orfanoudaki, A., & Weiner, R. B. (2020). Personalized treatment for coronary artery disease patients: a machine learning approach. Health care management science, 23(4), 482-506.
34. Makroum, M. A., Adda, M., Bouzouane, A., & Ibrahim, H. (2022). Machine learning and smart devices for diabetes management: Systematic review. Sensors, 22(5), 1843.
35. Rathore, S. P. S., Prajapat, D., Sharma, S., Pooja, S., Seth, R., & Sharma, G. (2024, November). Personalized Diabetes Management and Treatment Planning using Reinforcement Learning. In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE) (pp. 357-363). IEEE.
36. Gayaki, U., Daronde, S., Tale, A., & Barhate, A. (2025, February). A Review of Machine Learning Algorithms in Diabetes Management. In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL) (pp. 766-771). IEEE.
37. Rashid, M. M., Askari, M. R., Chen, C., Liang, Y., Shu, K., & Cinar, A. (2022). Artificial intelligence algorithms for treatment of diabetes. Algorithms, 15(9), 299.
38. Duckworth, C., Guy, M. J., Kumaran, A., O’Kane, A. A., Ayobi, A., Chapman, A., ... & Boniface, M. (2024). Explainable machine learning for real-time hypoglycemia and hyperglycemia prediction and personalized control recommendations. Journal of Diabetes Science and Technology, 18(1), 113-123.
39. Sugandh, F. N. U., Chandio, M., Raveena, F. N. U., Kumar, L., Karishma, F. N. U., Khuwaja, S., ... & Sugandh, F. (2023). Advances in the management of diabetes mellitus: a focus on personalized medicine. Cureus, 15(8).
Copyright (c) 2025 Hassan Tanveer, Muhammad Faheem, Arbaz Haider Khan, Muhammad Ali Adam
This work is licensed under a Creative Commons Attribution 4.0 International License.
