Enhancing Patient Care: Supervised Machine Learning in Personalized Nutrition Recommendation Systems

Session

Computer Science and Communication Engineering

Description

This paper explores the use of supervised machine learning techniques to develop a personalized diet recommendation system aimed at enhancing patient care. The system processes data gathered from patients, such as their health conditions, goals, and preferences, to suggest diets tailored to individual needs. Supervised learning algorithms, specifically Random Forest, are employed to train models that predict the most suitable diet for each patient based on historical data. The study demonstrates how machine learning can improve the quality and precision of dietary recommendations, ultimately leading to better patient outcomes. Index Terms—Healthcare, Machine Learning, Artificial Intelligence, Random Fores

Proceedings Editor

Edmond Hajrizi

Start Date

25-10-2024 9:00 AM

End Date

27-10-2024 6:00 PM

DOI

10.33107/ubt-ic.2024.410

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Oct 25th, 9:00 AM Oct 27th, 6:00 PM

Enhancing Patient Care: Supervised Machine Learning in Personalized Nutrition Recommendation Systems

This paper explores the use of supervised machine learning techniques to develop a personalized diet recommendation system aimed at enhancing patient care. The system processes data gathered from patients, such as their health conditions, goals, and preferences, to suggest diets tailored to individual needs. Supervised learning algorithms, specifically Random Forest, are employed to train models that predict the most suitable diet for each patient based on historical data. The study demonstrates how machine learning can improve the quality and precision of dietary recommendations, ultimately leading to better patient outcomes. Index Terms—Healthcare, Machine Learning, Artificial Intelligence, Random Fores