Show simple item record

dc.contributor.authorBishop, Thomas
dc.contributor.authorvon Hinke, S
dc.contributor.authorHollingsworth, B
dc.contributor.authorLake, AA
dc.contributor.authorBrown, H
dc.contributor.authorBurgoine, Thomas
dc.date.accessioned2022-02-03T01:56:01Z
dc.date.available2022-02-03T01:56:01Z
dc.date.issued2021-12-15
dc.identifier.issn2666-8270
dc.identifier.other34977839
dc.identifier.otherPMC8700226
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/333589
dc.description.abstractBackground and purpose Neighbourhood exposure to takeaway (‘fast’-) food outlets selling different cuisines may be differentially associated with diet, obesity and related disease, and contributing to population health inequalities. However research studies have not disaggregated takeaways by cuisine type. This is partly due to the substantial resource challenge of de novo manual classification of unclassified takeaway outlets at scale. We describe the development of a new model to automatically classify takeaway food outlets, by 10 major cuisine types, based on business name alone. Material and methods We used machine (deep) learning, and specifically a Long Short Term Memory variant of a Recurrent Neural Network, to develop a predictive model trained on labelled outlets (n=14,145), from an online takeaway food ordering platform. We validated the accuracy of predictions on unseen labelled outlets (n=4000) from the same source. Results Although accuracy of prediction varied by cuisine type, overall the model (or ‘classifier’) made a correct prediction approximately three out of four times. We demonstrated the potential of the classifier to public health researchers and for surveillance to support decision-making, through using it to characterise nearly 55,000 takeaway food outlets in England by cuisine type, for the first time. Conclusions Although imperfect, we successfully developed a model to classify takeaway food outlets, by 10 major cuisine types, from business name alone, using innovative data science methods. We have made the model available for use elsewhere by others, including in other contexts and to characterise other types of food outlets, and for further development.
dc.description.sponsorshipThis study is funded by the National Institute of Health Research (NIHR) School of Public Health Research (Grant Reference Number PD-SPH-2015). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was also supported by the MRC Epidemiology Unit, University of Cambridge (Grant Reference Number MC/UU/00006/7). TBu is funded by the Centre for Diet and Activity Research (CEDAR), a UK Clinical Research Collaboration (UKCRC) Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, the National Institute of Health Research, and the Wellcome Trust (Grant Reference Number MR/K023187/1), under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. These funders played no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.
dc.languageeng
dc.publisherElsevier
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcenlmid: 9918316881306676
dc.sourceessn: 2666-8270
dc.subjectClassification
dc.subjectData Science
dc.subjectCuisine Type
dc.subjectMachine (Deep) Learning
dc.subjectTakeaway (‘Fast-’) Food Outlets
dc.subjectUniversal Language Model Fine-tuning (Ulmfit)
dc.titleAutomatic classification of takeaway food outlet cuisine type using machine (deep) learning
dc.typeArticle
dc.date.updated2022-02-03T01:56:01Z
prism.publicationNameMachine Learning with Applications
prism.volume6
dc.identifier.doi10.17863/CAM.81006
dcterms.dateAccepted2021-07-05
rioxxterms.versionofrecord10.1016/j.mlwa.2021.100106
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidBishop, Thomas [0000-0002-3407-2526]
dc.contributor.orcidBurgoine, Thomas [0000-0001-6936-3801]
dc.identifier.eissn2666-8270
dc.publisher.urlhttps://www.sciencedirect.com/science/article/pii/S2666827021000530?via%3Dihub#!
pubs.funder-project-idMedical Research Council (MR/K023187/1)
pubs.funder-project-idDepartment of Health (via National Institute for Health Research (NIHR)) (PD-SPH-2015-10029 BH154142)
pubs.funder-project-idMRC (MC_UU_00006/7)
cam.issuedOnline2021-07-10


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International