Personalized Health and Nutrition Recommendations Using Machine Learning

Raut, Sanjay Rajendra (2024) Personalized Health and Nutrition Recommendations Using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

It is our vision that dependence on individualized nutrition is essential in order to positively impact overall health and reduce chronically debilitating diseases. The present dietary guideline for the population dies not take into account the lifestyle, age, gender, and activity level of the population. In this work, we present an approach to adopting a machine learning model for the prediction of the consumers’ dietary behaviours and recommending suitable diets. To achieve this aim, we employed the American Gut Project dataset to apply six Machine Learning models namely; Random Forest, Gradient Boosting, SVM, Neural Networks, CatBoost, and LightGBM. The Random Forest model, after hyperparameter optimization and SMOTE method, was found to be the most accurate classifier with overall accuracy of 73%, precision, recall and F1 score values all similar to each other. Our clustering analysis, performed with K-Means, revealed two distinct dietary patterns. More specifically the predetermined criteria were ‘Healthy’(High protein and fiber content) and ‘Unhealthy’ (High fat and carbohydrate content). Principal Component Analysis (PCA) was then used to represent these clusters and as seen, the clusters are separable. Some of the measures used in order to classify the diets included the frequency of exercise, the type of diet and age. This research closes the gap between the application of machine learning and personalized nutrition, with a complete program to analyse the intricacies of nutrition. Further work will focus on improving interpretable model solutions and utilizing them for the creation of tools to encourage people to adopt better diets.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Yaqoob, Abid UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning Q Science > QP Physiology > Nutrition R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine > Personal Health and Hygiene
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	04 Sep 2025 13:29
Last Modified:	04 Sep 2025 13:29
URI:	https://norma.ncirl.ie/id/eprint/8789

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