Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2019

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

Keywords

Glycemic Impact, Recipe Embeddings, Recipe Classification

Discipline

Databases and Information Systems | Health Information Technology | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

DPH 2019: Proceedings of the 9th International Conference on Digital Public Health: Marseille, France, November 20-23

First Page

31

Last Page

35

ISBN

9781450372084

Identifier

10.1145/3357729.3357748

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3357729.3357748

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