A Review on Well Fitness

Abstract

Personalized Nutrition is a red flag for the modern diet scheduling system when overcoming the limitations of the traditional one-size-fits-all diet. This milestone study presents for the first time an AI-powered Personalized Nutrition and Diet Management System that employs machine learning techniques on several bases such as KNN classifiers, feature selection, and model pipelines for real-time user-centric meal recommendations. The system provides real-time adaptations for greater flexibility in sticking to the plans altogether.

Introduction

Personalized nutrition is emerging as a modern breakthrough in improving physical and mental health by tailoring dietary recommendations to an individual’s unique genetic makeup, health status, metabolism, and lifestyle. Traditional one-size-fits-all diets often fail to address these differences, leading to poor health outcomes.

Advances in AI and machine learning have enabled smart nutrition systems that gather real-time data—such as health indicators, physical activity, and even environmental factors—to generate adaptive, personalized meal plans that evolve based on user input and changing needs.

The research aims to develop a scalable, user-friendly system that integrates nutrition science with modern technology to provide live, personalized dietary recommendations. Key objectives include creating an intuitive interface for user input, ensuring real-time adaptability of recommendations, maintaining high performance with large data volumes, and validating the system’s accuracy and usability.

Literature highlights that generic diet plans are ineffective for many due to metabolic and lifestyle variations. Personalized nutrition better supports individuals, especially those with conditions like obesity, diabetes, or genetic predispositions.

The system uses a three-tier architecture—presentation, application, and data layers—for scalability and maintainability, and employs technologies such as VS Code for development, NLP tools (spaCy, transformers) for understanding user queries, Angular for the frontend, cloud platforms (AWS/GCP) for storage and computation, real-time communication tools, and APIs for integration with external systems.

In conclusion, this AI-powered approach overcomes the static nature of conventional diet planning by continuously tailoring meal suggestions to the user’s evolving health profile, ensuring more effective and personalized nutrition support.

Conclusion

This research posits that AI can revolutionize dietary management with personalized nutrition recommendations. The meal-generating system works through the health profile, food preferences, and fitness goals of an individual using KNN classifiers, feature selection, model pipelines, and other machine learning techniques. Its most unique feature, that makes it different from static diet plans, is that it has real-time user-data dependent recommendations thereby allowing dynamic taking into account.

References

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Copyright

Copyright © 2025 Anshuman Mishra, Saurabh Pandey, Arun Singh, Rohan Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.