Personalized Diabetes Diet Recommendation System with Knowledge Graph and Incremental Learning

This paper presents a personalized diabetes diet recommendation system for Type 2 diabetes patients. The system integrates a knowledge graph to model food nutrition, ingredients, and recipes with a feedforward neural network to predict postprandial glucose levels. Experience replay is employed for incremental learning, allowing the system to adapt to individual health needs and preferences. Over 8 weeks, the system demonstrated improved prediction accuracy of 3 %, consistently high recipe diversity, nutritional allocation, and alignment with users' dietary preferences, indicating its potential to enhance diabetes management.