Intelligent Diet and Exercise Recommendation System Using AI

Abstract

Maintaining a healthy lifestyle is essential for preventing cardiovascular diseases, obesity, and metabolic disorders. Traditional diet plans often fail due to their generic nature and lack of real-time adaptability. This research presents an Intelligent Diet and Exercise Recommendation System that utilizes Optical Character Recognition (OCR), Natural Language Processing (NLP), and Machine Learning (ML) to provide personalized dietary recommendations based on cholesterol profiles extracted from medical reports. The approach includes automated cholesterol extraction, diet classification using ML models, and exercise recommendations via decision trees and K-Nearest Neighbors (KNN). A Spring Boot-based web application offers an interactive dashboard for real-time health insights. By leveraging predictive analytics and AI-driven insights, this project bridges the gap between static diet plans and personalized, dynamic health recommendations.