Abstract:
This project created and contrasted between Multi-Task Learning (MTL) and Single-Task Learning (STL) models in order to make personalized recommendations on nutrition based on an explainable AI method. Its overall goal was to design, implement, and test a predictive machine- learning model, which at once forecasts daily calorie consumption, protein, carbohydrates and fats at once based on individual-specific data, and explicitly compares MTL and STL architectures.The research utilized the publicly available Personalized Medical Diet Recommendations Dataset that consists of 5,000 individual records with the variables of demographic, clinical, lifestyle, dietary, and wearable-device. Complete records were kept after cleaning and preprocessing to be used in model training and testing. A multi-task deep-learning model with shared layers was trained parallel to four single-task deep-learning models. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and coefficient of determination (R2) were used to measure performance.Findings indicated that the MTL model performed better than the STL models when all four nutritional targets were used with the highest difference observed in protein prediction (R2 of 0.96 vs 0.65) and the overall efficiency of the parameters were achieved to about 48 percent. SHAP (SHapley Additive explanations) was incorporated to give an easy to understand feature-attribution explanation that showed BMI, weight, dietary habits, and current nutrient intake as the most significant predictors. According to the study, the performance of a multi-task learning model in individualized prediction combined with explainable AI is more accurate, efficient, and transparent than the use of the traditional single-task models. The framework which has been obtained offers an effective, reliable instrument of offering customized, evidence-based nutrition guidance that can help improve user comprehension and compliance.
