Diet(Nutrition) Recommender using Machine Learning

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 02 | Issue : 02 | Page : –
    By

    Nitesh Mali,

  • Sanket Katkade,

  • Prasad Aher,

  • Vivek Sarade,

  1. Student, Department of Computer Engineering, Parvatibai Genba Moze College Engineering Wagholi, Pune, Maharashtra, India
  2. Student, Department of Computer Engineering, Parvatibai Genba Moze College Engineering Wagholi, Pune, Maharashtra, India
  3. Student, Department of Computer Engineering, Parvatibai Genba Moze College Engineering Wagholi, Pune, Maharashtra, India
  4. Student, Department of Computer Engineering, Parvatibai Genba Moze College Engineering Wagholi, Pune, Maharashtra, India

Abstract

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In an era where maintaining a healthy lifestyle has become a priority, personalized dietary recommendations play a crucial role in guiding individuals towards achieving their nutritional goals. The Diet Recommendation System is an inno- vative application that combines user-specific data with machine learning algorithms to provide tailored dietary suggestions. This system is designed to simplify the process of meal planning and promote healthier eating habits. The Diet Recommendation System is developed as a Flutter application, ensuring cross-platform compatibility and a user- friendly interface. Users are prompted to input their physical characteristics, such as height, weight, age, gender, and activity level, as well as their desired number of meals per day. Lever- aging scientifically established formulas and global constants, the system calculates the recommended daily caloric intake and macronutrient distribution for each individual. The Diet Recommendation System aims to empower individu- als to make informed dietary choices by providing personalized recommendations based on their unique circumstances. By inte- grating machine learning algorithms with user-specific data and a comprehensive recipe database, the system offers a convenient and effective solution for meal planning and promoting healthier eating habits. Ultimately, this innovative approach has the poten- tial to contribute to the improvement of overall well–being and support individuals in achieving their health and fitness goals.

Index Terms—Machine Learning, Dietary Recommendations, Personalized Nutrition, K-Nearest Neighbors (KNN), Flutter Application, Cross-Platform, User-Friendly Interface, Caloric Intake, Macronutrient Distribution, Recipe Recommendations, FastAPI, Python, Client-Server Architecture, Meal Planning, Healthy Eating, Well-being.

Keywords: Machine Learning, Diet, Nutrition, KNN, BMI

[This article belongs to International Journal of Nutritions ]

How to cite this article:
Nitesh Mali, Sanket Katkade, Prasad Aher, Vivek Sarade. Diet(Nutrition) Recommender using Machine Learning. International Journal of Nutritions. 2025; 02(02):-.
How to cite this URL:
Nitesh Mali, Sanket Katkade, Prasad Aher, Vivek Sarade. Diet(Nutrition) Recommender using Machine Learning. International Journal of Nutritions. 2025; 02(02):-. Available from: https://journals.stmjournals.com/ijn/article=2025/view=0



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Regular Issue Subscription Original Research
Volume 02
Issue 02
Received 09/10/2024
Accepted 10/12/2024
Published 29/04/2025
Publication Time 202 Days
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