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In India, where thyroid-related disorders affect over 42 million people, particularly women, there is a pressing need for personalized dietary guidance. This paper proposes NutriGuide, a personalized food recommendation system that provides health-aware food suggestions to users based on their thyroid conditions. The system uses support vector machines for user classification and integrates alternative least square, Neural Collaborative Filtering (NCF), and variational autoencoders to generate effective recommendations. Among these, NCF demonstrated superior performance across evaluation metrics. A semi-personalized recommendation strategy using expert dietician ratings is proposed to tackle the cold-start problem. This approach provides a robust solution for enhancing nutrition awareness and delivering health-conscious food suggestions to users in resource-constrained settings.

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Health-Aware Food Recommendations for Thyroid Patients Using Machine Learning and Collaborative Filtering

  • 会议论文

  • 发表日期：

    2026年

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  • Mohan Bansal,
  • Ramesh Saha,
  • Gourav Jain

摘要

In India, where thyroid-related disorders affect over 42 million people, particularly women, there is a pressing need for personalized dietary guidance. This paper proposes NutriGuide, a personalized food recommendation system that provides health-aware food suggestions to users based on their thyroid conditions. The system uses support vector machines for user classification and integrates alternative least square, Neural Collaborative Filtering (NCF), and variational autoencoders to generate effective recommendations. Among these, NCF demonstrated superior performance across evaluation metrics. A semi-personalized recommendation strategy using expert dietician ratings is proposed to tackle the cold-start problem. This approach provides a robust solution for enhancing nutrition awareness and delivering health-conscious food suggestions to users in resource-constrained settings.