Adaptive Diet Analysis: Connecting Individuals to Personalized Nutrition Solutions

Abstract

The rapid advancement of digital technologies has opened new possibilities for personalized health management, particularly in the field of nutrition. However, traditional diet planning continues to suffer from generalization, lack of personalization, and minimal integration of user-specific factors, limiting the effectiveness of nutritional guidance. To overcome these limitations, we propose Adaptive Diet Analysis, a smart dietary recommendation platform designed to deliver personalized nutrition plans tailored to an individual's unique physiological, lifestyle, and health attributes. By leveraging user input and machine learning, this system generates customized calorie recommendations, nutrient breakdowns, and meal suggestions, eliminating guesswork and enhancing nutritional awareness. Features such as real-time diet personalization, intelligent meal mapping, and a comprehensive nutrient database ensure accuracy while improving user engagement and dietary adherence. Unlike existing health apps that lack deep personalization or fail to consider multiple health factors simultaneously, Adaptive Diet Analysis introduces a robust prediction model using Random Forest Regressor, an intuitive web interface, and modular scalability for future integration with wearable devices or fitness apps. By incorporating emerging technologies such as machine learning for dietary prediction and real-time user profiling, the platform reduces planning inefficiencies, avoids generic recommendations, and empowers users to achieve specific health goals. Empirical results and prototype evaluations demonstrate its potential to transform nutritional planning, ensuring accessible, personalized, and effective diet guidance for diverse individuals.