AI in Nutrition: Using Machine Learning to Provide Tailored Food Suggestions and Improve Public Health

The rise of diet-linked conditions such as obesity and cardiovascular disease has made precise, individualized nutrition a cornerstone of modern preventive medicine. Our research addresses this by analyzing a comprehensive dataset of 8,789 food items, each characterized by 77 distinct nutritional markers ranging from basic macronutrients to specific fatty acids and vitamins. During preprocessing, we standardized the data and established five core dietary categories: High Protein, Low Calorie, High Fat, High Fiber, and Balanced. To combat significant class imbalances, we utilized SMOTE and Stratified Shuffle Split, ensuring our 80/20 training-validation split remained representative. We then engineered a deep learning model via TensorFlow/Keras featuring a multi-branch architecture and an Attention Layer to optimize feature selection. The model achieved near-perfect metrics—approximately 99% accuracy and an AUC of 1.00—demonstrating its strength in automated food classification. Our future efforts will focus on validating these results against external clinical datasets to refine their utility in real-world healthcare settings. The overall findings demonstrated deep learning's enormous potential for automated food classification and tailored dietary advice. Validation using external datasets and additional optimization for improvement in practical healthcare and nutrition planning will be the main goals of future work.