AI-POWERED PERSONALIZED DIET RECOMMENDATION SYSTEM BASED ON USER HEALTH PROFILES

Abstract

The increasing prevalence of lifestyle-related
diseases such as obesity, diabetes, and hypertension
has created a growing demand for intelligent and
personalized dietary solutions. Traditional diet
plans often fail to consider individual differences
such as age, gender, Body Mass Index (BMI),
medical history, and lifestyle habits, resulting in
ineffective outcomes. This project presents an AIpowered
personalized diet recommendation system
that leverages user health profiles to generate
customized nutritional plans. The system collects
user-specific data including height, weight, dietary
preferences, allergies, and health conditions, and
applies artificial intelligence techniques to analyze
nutritional requirements and recommend balanced
diet plans. A structured nutritional database is used
to match food items with calculated calorie and
nutrient needs, ensuring accuracy and
personalization. The system is implemented using
Python and Django for backend processing, while
SQLite is used for efficient data storage and
retrieval. The web-based interface enables users to
register, manage profiles, receive
recommendations, and provide feedback. The
feedback mechanism enhances system adaptability
by improving recommendation accuracy over time.
The proposed system reduces dependency on
manual diet consultations and ensures accessibility,
scalability, and cost-effectiveness. By integrating
AI with nutrition science, the system promotes
healthy eating habits and preventive healthcare.
Overall, this project demonstrates how intelligent
systems can transform dietary planning into a datadriven,
automated, and personalized solution,
contributing significantly to improving individual
health and well-being