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arXiv · 1909.07881

Estimating Glycemic Impact of Cooking Recipes via Online Crowdsourcing and Machine Learning

Helena Lee·Palakorn Achananuparp·Yue Liu·Ee-Peng Lim·Lav R. Varshney
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Abstract

Consumption of diets with low glycemic impact is highly recommended for diabetics and pre-diabetics as it helps maintain their blood glucose levels. However, laboratory analysis of dietary glycemic potency is time-consuming and expensive. In this paper, we explore a data-driven approach utilizing online crowdsourcing and machine learning to estimate the glycemic impact of cooking recipes. We show that a commonly used healthiness metric may not always be effective in determining recipes suitable for diabetics, thus emphasizing the importance of the glycemic-impact estimation task. Our best classification model, trained on nutritional and crowdsourced data obtained from Amazon Mechanical Turk (AMT), can accurately identify recipes which are unhealthful for diabetics.

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Helena Lee, Palakorn Achananuparp, Yue Liu, Ee-Peng Lim, Lav R. Varshney. 2019-09-17. Estimating Glycemic Impact of Cooking Recipes via Online Crowdsourcing and Machine Learning. https://doi.org/10.1145/3357729.3357748

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