Integrating nutritional deficiency prediction with semantic recipe intelligence: A machine learning approach
Nutritional deficiencies remain a significant global public health challenge, while existing dietary assessment and recipe recommendation systems often operate independently, limiting their ability to provide integrated and personalized nutritional guidance. This article presents an integrated artificial intelligence framework developed using Sri Lankan dietary reference intake guidelines, culturally specific food composition data, and a curated corpus of Sri Lankan recipes to support household nutritional guidance. The proposed framework was developed as part of this Sri Lankan study to address local nutritional challenges while providing a methodology that can be adapted to other countries by replacing country-specific dietary reference standards, food composition databases, and recipe repositories with those of other countries. The nutritional model employed adequacy ratio-based features with a random forest classifier, achieving 88.12% accuracy and a macro area under the curve of 0.91. The semantic module used Sentence-Bidirectional Encoder Representations from Transformers embeddings with fuzzy ingredient matching to achieve 86.84% classification accuracy under stratified cross-validation. By linking predicted deficiencies to context-aware recipes, the system transforms analytical insights into actionable meal recommendations. The results demonstrated that the framework achieved stable performance and showed strong potential for practical application.