Type 2 Diabetes

Type 2 diabetes research centers on improving diagnosis, treatment, and management, focusing on personalized approaches to mitigate its significant health and economic burden. Current research employs machine learning models, including neural networks (like LSTMs and CNNs), Bayesian networks, and ensemble methods, to analyze diverse data sources such as electronic health records, continuous glucose monitoring data, and retinal images, aiming to predict risk, personalize insulin dosing, and optimize patient care. These data-driven approaches, often combined with expert knowledge and explainable AI techniques, are improving the accuracy and efficiency of diabetes management, leading to better patient outcomes and more effective resource allocation. The ultimate goal is to develop more precise, accessible, and equitable strategies for preventing and treating this prevalent disease.