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Consumption of diets with low glycemic impact is highly recommended fordiabetics and pre-diabetics as it helps maintain their blood glucose levels.However, laboratory analysis of dietary glycemic potency is time-consuming andexpensive. In this paper, we explore a data-driven approach utilizing onlinecrowdsourcing and machine learning to estimate the glycemic impact of cookingrecipes. We show that a commonly used healthiness metric may not always beeffective in determining recipes suitable for diabetics, thus emphasizing theimportance of the glycemic-impact estimation task. Our best classificationmodel, trained on nutritional and crowdsourced data obtained from AmazonMechanical Turk (AMT), can accurately identify recipes which are unhealthfulfor diabetics.

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