Estimating glycemic impact of cooking recipes via online crowdsourcing and machine learning

• Main Authors: LEE, Helena, ACHANANUPARP, Palakorn, LIU, Yue, LIM, Ee-Peng, VARSHNEY, Lav R.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Glycemic Impact; Recipe Embeddings; Recipe Classification; Databases and Information Systems; Health Information Technology; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/4723; https://ink.library.smu.edu.sg/context/sis_research/article/5726/viewcontent/GI_cooking_crowdsourcing_av.pdf
• Institution: Singapore Management University

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