Publication Details
Abstract
Diabetes mellitus, a chronic metabolic disorder, affects millions globally, necessitating improved understanding and management strategies. Mathematical modeling provides an effective tool for analyzing the complex dynamics of glucose regulation, insulin response, and associated metabolic processes. This paper presents a comprehensive review of the mathematical models applied to diabetes, focusing on glucose-insulin feedback systems, long-term complications, and treatment optimization. We explore deterministic and stochastic models that simulate the progression of diabetes, including Type 1 and Type 2, and discuss their relevance in personalized medicine. By integrating physiological insights into mathematical frameworks, these models offer significant potential for enhancing diabetes management through predictive diagnostics and optimized treatment plans.