Dietary Recommendation System Based on Machine Learning

G. Lucy, K. Meghana, Damagatla Sowmya, G. Rajeswari, K. Suma Latha

2025

Abstract

In order to make nutrition recommendations based on machine learning, this research employs a Decision Tree Classifier. Dietary restrictions, physical activity levels, age, gender, BMI, disease type, severity, and training data are all inputs into the model. The system accurately predicts outcomes, encodes categorical factors, and partitions the dataset for testing and training. Diet recommendations tailored to the user's input can be made in real-time by saving the trained model. For optimal health, the model suggests tailored meal patterns that include Low-Carb, Low-Sodium, and Balanced foods.

Paper Citation

in Harvard Style

Lucy G., Meghana K., Sowmya D., Rajeswari G. and Latha K. (2025). Dietary Recommendation System Based on Machine Learning. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 278-282. DOI: 10.5220/0013911800004919

in Bibtex Style

@conference{icrdicct`2525,
author={G. Lucy and K. Meghana and Damagatla Sowmya and G. Rajeswari and K. Latha},
title={Dietary Recommendation System Based on Machine Learning},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={278-282},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013911800004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Dietary Recommendation System Based on Machine Learning
SN - 978-989-758-777-1
AU - Lucy G.
AU - Meghana K.
AU - Sowmya D.
AU - Rajeswari G.
AU - Latha K.
PY - 2025
SP - 278
EP - 282
DO - 10.5220/0013911800004919
PB - SciTePress