AI-based Dietary Monitoring and Biometric Health Prediction

The personalized, proactive health tracking of diet would be even more important because of the rising burden of the chronic diseases like diabetes, hypertension, and cardiovascular disease. The present research proposes an AI-powered system that observes the relation between ingredients-level consumption of food and major health markers such as cholesterol, blood glucose, and blood pressure. It combines the methods of natural language processing and computer vision to automatically derive the accurate nutrition data of food records, labels, food images and meal photographs. It predicts the physiological effects of certain ingredients with high accuracy using machine learning approaches, such as, LSTM and XGBoost as well as real time wearable sensor measurements. The data fusion between the textual, visual and biometric data increases reliability of predictions using multi-modal data fusion. Also, explainable AI tools like SHAP and LIME will offer insight regarding model decisions, and individual dietary advice will be improved with reinforcement learning and collaboration filtering. All of the suggestions have been made in keeping with, global dietary guidelines, so they do prove to be safe and medically relevant. The organic, flexible system proposed on the other hand provides a scalable flexible solution to chronic disease management, preventative healthcare, and personalised nutrition planning.