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Year 2024, , 438 - 447, 01.08.2024
https://doi.org/10.55549/epstem.1523634

Abstract

References

  • Shyti, B., Stergu, A., Valera, D., & Blerina, P. (2024). Food recommendation system for a healthy liver using machine learning. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 28, 438-447.

Food Recommendation System for a Healthy Liver Using Machine Learning

Year 2024, , 438 - 447, 01.08.2024
https://doi.org/10.55549/epstem.1523634

Abstract

Nowadays, the work routine, the problems we face, make us not pay proper attention to healthy eating. As a result, people get sick. We have created through mathematical knowledge (statistics, probability, linear algebra, geometry), combining the concepts of Machine Learning (Content Based Filtering, TD IDF Vectorizer Algorithm, Conditional Independence, Count Vectorization) with Python language, a Recommendation System for all people suffering from liver. Liver is one of the most important organs of our body, because it makes 500 essential functions in the organism. But we have to be aware about the types of food that we use. Also, we must be very careful with our lifestyle and diet on foods. The aim of this article is to recommend the most appropriate foods for a healthy liver. This article will help the p

References

  • Shyti, B., Stergu, A., Valera, D., & Blerina, P. (2024). Food recommendation system for a healthy liver using machine learning. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 28, 438-447.
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Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Articles
Authors

Shyti Bederiana

Stergu Argesta

Valera Dhurata

Papajani Blerina

Early Pub Date July 29, 2024
Publication Date August 1, 2024
Submission Date February 7, 2024
Acceptance Date April 17, 2024
Published in Issue Year 2024

Cite

APA Bederiana, S., Argesta, S., Dhurata, V., Blerina, P. (2024). Food Recommendation System for a Healthy Liver Using Machine Learning. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 28, 438-447. https://doi.org/10.55549/epstem.1523634