SMART DIET PLANNER USING AI FOR PERSONALIZED HEALTH RECOMMENDATIONS WITH CHATBOT

Abstract

Proper nutrition and personalized dietary guidance play a crucial role in preventing lifestyle-related diseases, maintaining healthy body composition, and improving overall physical and mental well-being. In recent years, the rapid development of Artificial Intelligence (AI) and intelligent healthcare technologies has enabled the design of smart nutritional systems capable of providing customized dietary recommendations based on individual health requirements. Conventional diet planning systems generally provide generic meal suggestions that may not adequately consider personal health conditions, dietary preferences, body composition, nutritional requirements, or regional food availability. Such limitations can reduce the relevance of recommended meal plans, user engagement, and long-term adherence to healthy dietary practices. To address these challenges, the proposed Smart Diet Planner introduces an intelligent and personalized framework for generating region-specific dietary recommendations. The system utilizes a structured nutritional database containing regional food items, calorie values, macronutrient compositions, dietary categories, user health profiles, lifestyle characteristics, and medical conditions collected from standard nutritional resources and userprovided information. During preprocessing, the collected data undergoes data validation, missing-value handling, duplicate removal, Body Mass Index (BMI) calculation, feature categorization, normalization of health-related parameters, and regional food mapping to enhance data consistency and recommendation quality. The proposed framework integrates a rule-based diet recommendation engine, BMI-based health assessment module, rule-based health risk prediction mechanism, and an AI-powered chatbot for providing personalized nutritional guidance and immediate responses to health-related queries. Based on individual health profiles and dietary requirements, the system generates suitable meal recommendations while considering regional food preferences and nutritional constraints. The performance of the proposed system is evaluated using recommendation accuracy, precision, recall, F1-score, response time, and user satisfaction metrics. Experimental evaluation demonstrates that the integrated personalized recommendation framework consistently produces relevant and nutritionally appropriate region-specific meal plans, achieving improved recommendation accuracy, faster response times, and enhanced user interaction compared with conventional generic diet planning approaches. The proposed Smart Diet Planner ultimately enhances dietary personalization, promotes health awareness, encourages healthier eating habits, and supports informed nutritional decision-making through intelligent, accessible, and interactive AI-driven dietary recommendations.