Intelligent Interactive Dietary Recommendation Systems Using Nlp

Abstract

Diet also plays a role in overall health and well- being, particularly in patients with conditions like diabetes. With the increase in nutritional health concerns, there is an increasing demand for individualized nutrition guidance depending on an individual's health, lifestyle and nutritional needs. Current nutrition recommendation systems are primarily concerned with food categorization with convolutional neural networks (CNNS), but they propose personalization, real- time user engagement or flexibility to local food culture. Our feeding planning system supports individualized food planning according to user-specific parameters like age, weight, medical history, eating behavior and activity level, based on the integration of artificial neuronal networks (ANN) and natural language processing (NLP). The system predicts the risk of diabetes by employing ML-based predictive models for maximizing food planning and dynamic nutrition planning. NLP facilitates an interactive interface whereby users can choose and get recommended meals, nutritional facts and recipes, and macronutrients. The nutrition tracking feature also tracks the user's food consumption, with real-time feedback and progress tracking.This work illustrates the capability of ML and NLP to be used in digital health interventions and demonstrates how data-driven nutrition recommendations can lead to healthy diets. By continuously adapting to the user's behavior and physiological changes, the system proposed here in provides users with the freedom to select healthier foods for improved health and disease prevention.