Estimating Glycemic Impact of Cooking Recipes via Online Crowdsourcing and Machine Learning
This work addresses the need for efficient glycemic impact estimation for diabetics and pre-diabetics, though it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of estimating the glycemic impact of cooking recipes for diabetics by using online crowdsourcing and machine learning, achieving accurate identification of unhealthful recipes with their best model.
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.