Neural-KMeans-Based Food Feature Modeling for Personalized Nutrition Plans

Abstract

Personalized nutrition represents a transformative approach in health science that aims to tailor dietary recommendations based on individual characteristics, preferences, and metabolic responses. As the demand for precision health management increases, the integration of artificial intelligence and machine learning techniques in nutrition science has become pivotal. This paper introduces a hybrid model combining Neural Networks and K-Means Clustering (Neural-KMeans) to construct an effective food feature modeling system aimed at developing personalized nutrition plans. The proposed system processes dietary data, extracts complex nutritional features, and clusters user-specific dietary patterns to recommend optimized nutrition strategies. The integration of neural networks allows the model to capture non-linear relationships in nutritional attributes, while K-Means aids in categorizing food types and user consumption habits. An experimental framework was established using a real-world dietary dataset, and results demonstrate significant improvement in accuracy and personalization compared to traditional models. The study underscores the potential of Neural-KMeans modeling in revolutionizing dietary planning and facilitating health-conscious decision-making.