Automatic classification of takeaway food outlet cuisine type using machine (deep) learning.
Machine learning with applications 2021 ; 6: None.
Bishop TRP, von Hinke S, Hollingsworth B, Lake AA, Brown H, Burgoine T
DOI : 10.1016/j.mlwa.2021.100106
PubMed ID : 34977839
PMCID : PMC8700226
URL : https://linkinghub.elsevier.com/retrieve/pii/S2666827021000530
Researchers have not disaggregated neighbourhood exposure to takeaway ('fast-') food outlets by cuisine type sold, which would otherwise permit examination of differential impacts on diet, obesity and related disease. This is partly due to the substantial resource challenge of 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.
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 4,000) from the same source.
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.
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.