Food-Drug Interaction and Allergy Prediction System Using Machine Learning

Abstract

Problem: Individuals with allergies often struggle to identify potential allergic reactions when consuming packaged food or taking multiple medications, especially in cases where ingredient lists are complex or not clearly understood. Existing systems typically focus on either drug-drug interactions or single-allergen predictions, lacking a comprehensive, personalized approach. This research addresses this gap by developing a system that predicts potential allergic reactions caused by interactions between food and medicine ingredients, considering individual-specific health data such as gender and blood type. Methodology: The system utilizes Optical Character Recognition (OCR) to extract food label text and combines it with user-entered medication details. Two machine learning models are developed—one for predicting allergy severity using Gradient Boosting, and another for predicting allergy descriptions using XGBoost and Random Forest. The dataset is preprocessed using normalization and SMOTE for balancing. Personalized features such as gender and blood group are integrated to improve model accuracy. The system is built as a web-based platform using React.js for the front end and a Python backend for model execution and prediction. Initial Result: The allergy severity prediction model achieved an accuracy of 96.27%, while the allergy description prediction model achieved 84.01% (XGBoost) and 77.52% (Random Forest). OCR accuracy depends on label clarity and formatting but generally performs well under clean image conditions. Although results show strong initial performance, improvements are planned for label variability handling, ingredient name normalization, and large-scale testing with real world data.