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- W4313492039 abstract "Dietary interventions are necessary to lead a healthy life. Most of these interventions are done by adjusting daily macronutrient intake. Assessing daily macronutrient intake for users based on their health profile is a tedious task. In this paper, a supervised learning-based system to predict macronutrient categories is presented. The system is built using synthetic data generated from live user profiles. The generated dataset is validated and annotated with macronutrient category labels. Along with the category labels, the system also presents reasons for the prediction. Various experiments pertaining to the efficacy and trustworthiness of the models are performed and presented. Results suggest that macronutrient category prediction and its interpretability are highly reliable and could be easily adopted by the healthcare industry." @default.
- W4313492039 created "2023-01-06" @default.
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- W4313492039 date "2023-01-04" @default.
- W4313492039 modified "2023-09-27" @default.
- W4313492039 title "Paradigm shift in Nutritional Science: Using Machine Learning to Predict Macronutrient Requirements" @default.
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- W4313492039 doi "https://doi.org/10.1145/3570991.3571054" @default.
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