Neural Network-Assisted Diet Recommendation Using Nutritional Clustering

Abstract

The growing prevalence of diet-related health issues such as obesity, diabetes, and cardiovascular diseases necessitates the development of intelligent, personalized dietary recommendation systems. Traditional diet plans often fail to accommodate individual nutritional needs and preferences, leading to suboptimal health outcomes. In this study, we propose a novel Neural Network-Assisted Diet Recommendation (NNADR) framework that utilizes nutritional clustering to deliver personalized and adaptive meal suggestions. By leveraging unsupervised learning techniques to cluster foods based on their nutritional profiles and combining this with a supervised neural network model to map user profiles to appropriate food clusters, the system offers highly individualized diet plans. Extensive experiments were conducted on a comprehensive nutritional dataset, evaluating the model’s ability to recommend diets aligned with user health goals and constraints. The proposed approach achieved superior performance compared to baseline methods, demonstrating the efficacy of combining clustering and neural networks for diet recommendation. This work contributes to the advancement of intelligent health management systems and opens pathways for more holistic and personalized nutrition planning.