Vol. 5 No. 1 (2025): Issue 5
Articles

The Model of Food Nutrition Feature Modeling and Personalized Diet Recommendation Based on the Integration of Neural Networks and K-Means Clustering

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  • Pei-Min Lu,
  • Zhaoyan Zhang
Pei-Min Lu
Reckitt Manon, Shanghai, 200000, China
Zhaoyan Zhang
Zhongke Zhidao (Beijing) Technology Co., Ltd. Beijing

Published 2025-02-11

Keywords

  • Personalized diet recommendation,
  • Neural network,
  • K-means clustering

How to Cite

Lu, P.-M., & Zhang, Z. (2025). The Model of Food Nutrition Feature Modeling and Personalized Diet Recommendation Based on the Integration of Neural Networks and K-Means Clustering. Journal of Computational Biology and Medicine, 5(1). Retrieved from https://journal.mri-pub.com/index.php/JCBM/article/view/60

Abstract

In recent years, the growing awareness of health-conscious eating habits and the rising prevalence of lifestyle diseases, such as obesity and diabetes, have underscored the importance of personalized nutrition. As individuals face increasingly complex dietary choices, the need for systems that can recommend food based on specific nutritional requirements has become crucial. Traditional food recommendation methods are often limited by their inability to account for individual health needs and preferences. This study proposes a novel personalized diet recommendation model that combines neural networks and K-Means clustering. K-Means is used to cluster food items based on their nutritional content, while a neural network predicts the most appropriate cluster for an individual based on their input nutritional preferences. Given a specific set of nutritional requirements, the system can return a list of foods that meet these criteria, enhancing its practical application for personalized diet planning. The model incorporates data preprocessing techniques such as handling missing values, standardizing nutritional features, and selecting relevant features to improve efficiency and accuracy. The results show that the model is effective in recommending foods that align closely with the user's dietary goals, with performance evaluated through multiple machine learning metrics. This hybrid model significantly enhances the accuracy and relevance of food recommendations compared to traditional methods, offering a promising solution for personalized diet planning. Future improvements could focus on integrating dynamic user profiles and incorporating additional health factors, such as micronutrients and individual health conditions, to provide even more personalized recommendations.

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