Artificial Intelligence-Based Nonlinear Mathematical Modeling and Control of Glucose-Insulin Dynamics in Type 2 Diabetic Patients


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Authors

  • Vivek Jayan Assistant Professor, Department of Community Medicine,Saveetha Medical College and Hospitals, SIMATS, Thandalam, Chennai, Tamilnadu, India.
  • Boborayimov Okhunjon Khushmurod ugli Department of Information Processing and Management Systems, Tashkent State Technical University, Tashkent, 100095.
  • Gulirano Khodjieva Assistant of the Department of Propedeutics of Internal Diseases, Bukhara state medical university named after Abu Ali ibn Sino, Uzbekistan
  • Sindhu Shankar S Assistant Professor, Department of Community Medicine, Aduchunchungiri University,NAGARUR, Nelamangala, Bangalore, India.
  • Ulugbek Bobamuratov PhD,Department of Information Technology and Exact Sciences, Termez University of Economics and Service, Termez, Uzbekistan.
  • K.Natarajan Assistant Professor, Department of Biomedical Engineering, Vinayaka Mission's Kirupananda Variyar Engineering College, Salem, (Vinayaka Mission's Research Foundation), Tamilnadu, India.
  • A.Malarvizhi Assistant professor, Department of Electronics and Communication Engineering, Vinayaka Mission`s Kirupananda Variyar Engineering College, Salem. (Vinayaka Mission`s Research Foundation)

Keywords:

Glucose–insulin modeling, Type 2 diabetes mellitus, Nonlinear dynamics, Artificial intelligence, Reinforcement learning, Predictive control, Physiological modeling

Abstract

The relationship between glucose and insulin regulation in type 2 diabetes mellitus (T2DM) is non-linear and highly dynamic and cannot be dealt with by exact modelling and smart control. The proposed study is a combination of artificial intelligence (AI)-based nonlinear mathematical modelling and control in addressing glucose insulin dynamics in diabetic patients with type 2 diabetes (T2DM). It begins with the construction of a nonlinear physiological model based on lengthy principles of minimal modelling of Bergman of the absorption of glucose into the body and the secretion of insulin and peripheral uptake in the face of pathological insulin resistance. Machine learning-based adaptive estimators are also used to further optimise the model parameters in capturing the inter-individual physiological variability. Subsequently, a hybrid type of control that involves both model predictive control (MPC) and reinforcement learning (RL) is created to control exogenous insulin delivery in the face of meals and metabolic disturbances. The results of the simulation prove that the system proposed will have a much higher level of glucose regulation, less postprandial hyperglycemia, and a stronger response to parameter uncertainty than the traditional proportional-integral-derivative (PID) and classical MPC plans. The AI-enhanced model predicts the glucose kinetics accurately and it attains a stable control without causing the hypoglycemia. The results demonstrate how AI-based nonlinear models can be effective in aiding patient-specific closed-loop insulin therapy and that this can provide a viable direction toward real-time individualized diabetes treatment.

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Published

2025-12-28

How to Cite

Vivek Jayan, Boborayimov Okhunjon Khushmurod ugli, Gulirano Khodjieva, Sindhu Shankar S, Ulugbek Bobamuratov, K.Natarajan, & A.Malarvizhi. (2025). Artificial Intelligence-Based Nonlinear Mathematical Modeling and Control of Glucose-Insulin Dynamics in Type 2 Diabetic Patients. Results in Nonlinear Analysis, 8(3), 180–189. Retrieved from https://nonlinear-analysis.com/index.php/pub/article/view/795

Issue

Vol. 8 No. 3 (2025): Volume 8 issue 3

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Articles

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