Personalized Meal Recommendation System for Diabetes Management

Diabetes is one of the most prevalent chronic diseases worldwide, requiring continuous monitoring of blood glucose levels and strict dietary management. Improper meal planning can lead to severe complications such as hyperglycemia and hypoglycemia. This paper proposes a Personalized Meal Recommendation System for Diabetes that leverages machine learning, nutritional databases, and user health profiling to generate customized meal plans. The system considers factors such as blood glucose levels, body mass index (BMI), age, physical activity, dietary preferences, and medical history. A recommendation engine predicts suitable meals while maintaining balanced carbohydrate intake and glycemic index control. Experimental evaluation demonstrates improved dietary adherence, glucose stability, and user satisfaction. The proposed system provides an intelligent, scalable, and accessible solution for diabetes dietary management. A personalized meal recommendation system is proposed to support diabetic patients in managing their condition through tailored dietary plans. The system leverages collaborative filtering and content-based filtering techniques to recommend healthy meals based on individual nutritional requirements, health goals, and food preferences. By integrating with healthcare data and adapting to patient progress, the system aims to ensure glycemic control and overall well-being. The proposed system has the potential to improve patient engagement and adherence to dietary plans, ultimately enhancing quality of life for diabetics. By providing personalized meal recommendations, the system can help patients make informed food choices and manage their condition effectively. The system’s effectiveness will be evaluated through user feedback and health outcome metrics, paving the way for future enhancements and broader applications in diabetes management.