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

Helena Lee, Palakorn Achananuparp, Yue Liu, Ee Peng Lim, Lav R. Varshney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationDPH 2019 - Proceedings of the 9th International Conference on Digital Public Health
PublisherAssociation for Computing Machinery
Pages31-35
Number of pages5
ISBN (Electronic)9781450372084
DOIs
StatePublished - Nov 20 2019
Event9th International Conference on Digital Public Health, DPH 2019 - Marseille, France
Duration: Nov 20 2019Nov 23 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference9th International Conference on Digital Public Health, DPH 2019
CountryFrance
CityMarseille
Period11/20/1911/23/19

Keywords

  • Glycemic Impact
  • Recipe Classification
  • Recipe Embeddings

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Lee, H., Achananuparp, P., Liu, Y., Lim, E. P., & Varshney, L. R. (2019). Estimating glycemic impact of cooking recipes via online crowdsourcing and machine learning. In DPH 2019 - Proceedings of the 9th International Conference on Digital Public Health (pp. 31-35). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3357729.3357748