Enhancing Diabetic Retinopathy Management Through Personalized Recommendations with Reinforcement Learning

Diabetic retinopathy (DR) is one of the main causes of vision impairment among diabetes patients, which leads to the requirement of effective management strategies to stop its progression. In traditional management methods, this process is often less personalized, which results in poor dietary and exercise practices among patients. This paper presents a mobile application, “Retina-Care,” created to give out personalized dietary and exercise recommendations using Q-Iearning - a method of reinforcement learning. This proposed system dynamically learns and adapts to individual user behaviors, which leads to users engaging with the system more and adhering to the recommendations given. The paper addresses the cold start problem which can be seen in reinforcement learning systems, through a combination of epsilon-greedy methods, initialized Q-tables, and integration of an expert system. The results received through simulations and user testing indicate that the proposed strategy was successful in giving recommendations that were suitable to the unique medical conditions and preferences of the diabetic retinopathy patients. The said findings show that reinforcement learning can be used to enhance the effectiveness of diabetic retinopathy management systems, offering a promising solution for broader health care applications.