COMPUTATIONAL MINING OF NUTRITIONAL COMPONENT FOR NON- COMMUNICABLE DISESAE ANALYSIS

Suitable nutritional diets have been widely recognized as important measures to prevent and control non-communicable diseases (NCDs). However, there is little research on nutritional ingredients in food now, which are beneficial to the rehabilitation of NCDs. The rising global prevalence of Non-Communicable Diseases (NCDs) such as diabetes, cardiovascular diseases, and obesity highlights the urgent need for accessible preventive healthcare tools. This project presents an interactive web-based application designed to analyse user’s dietary habits and assess their potential risk for developing NCDs based on nutritional intake. The system allows users to select consumed food items, input quantities, and receive a detailed nutrient breakdown. By comparing these values against recommended thresholds, the application identifies potential NCD risks and presents results through intuitive visualizations, including graphs, for easy interpretation. A key feature of this platform is its AI-powered chatbot, which provides real-time nutritional guidance and answers user queries about food and health. Additionally, an educational module offers comprehensive information on common NCDs including causes, symptoms, and dietary recommendations promoting awareness and healthier lifestyle choices.