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Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach

Consumer food environments have transformed dramatically in the last decade. Food outlet prevalence has increased, and people are eating food outside the home more than ever before. Despite these developments, national spending on food control has reduced. The National Audit Office report that only...

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Autores principales: Oldroyd, Rachel A., Morris, Michelle A., Birkin, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8656817/
https://www.ncbi.nlm.nih.gov/pubmed/34886362
http://dx.doi.org/10.3390/ijerph182312635
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author Oldroyd, Rachel A.
Morris, Michelle A.
Birkin, Mark
author_facet Oldroyd, Rachel A.
Morris, Michelle A.
Birkin, Mark
author_sort Oldroyd, Rachel A.
collection PubMed
description Consumer food environments have transformed dramatically in the last decade. Food outlet prevalence has increased, and people are eating food outside the home more than ever before. Despite these developments, national spending on food control has reduced. The National Audit Office report that only 14% of local authorities are up to date with food business inspections, exposing consumers to unknown levels of risk. Given the scarcity of local authority resources, this paper presents a data-driven approach to predict compliance for newly opened businesses and those awaiting repeat inspections. This work capitalizes on the theory that food outlet compliance is a function of its geographic context, namely the characteristics of the neighborhood within which it sits. We explore the utility of three machine learning approaches to predict non-compliant food outlets in England and Wales using openly accessible socio-demographic, business type, and urbanness features at the output area level. We find that the synthetic minority oversampling technique alongside a random forest algorithm with a 1:1 sampling strategy provides the best predictive power. Our final model retrieves and identifies 84% of total non-compliant outlets in a test set of 92,595 (sensitivity = 0.843, specificity = 0.745, precision = 0.274). The originality of this work lies in its unique and methodological approach which combines the use of machine learning with fine-grained neighborhood data to make robust predictions of compliance.
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spelling pubmed-86568172021-12-10 Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach Oldroyd, Rachel A. Morris, Michelle A. Birkin, Mark Int J Environ Res Public Health Article Consumer food environments have transformed dramatically in the last decade. Food outlet prevalence has increased, and people are eating food outside the home more than ever before. Despite these developments, national spending on food control has reduced. The National Audit Office report that only 14% of local authorities are up to date with food business inspections, exposing consumers to unknown levels of risk. Given the scarcity of local authority resources, this paper presents a data-driven approach to predict compliance for newly opened businesses and those awaiting repeat inspections. This work capitalizes on the theory that food outlet compliance is a function of its geographic context, namely the characteristics of the neighborhood within which it sits. We explore the utility of three machine learning approaches to predict non-compliant food outlets in England and Wales using openly accessible socio-demographic, business type, and urbanness features at the output area level. We find that the synthetic minority oversampling technique alongside a random forest algorithm with a 1:1 sampling strategy provides the best predictive power. Our final model retrieves and identifies 84% of total non-compliant outlets in a test set of 92,595 (sensitivity = 0.843, specificity = 0.745, precision = 0.274). The originality of this work lies in its unique and methodological approach which combines the use of machine learning with fine-grained neighborhood data to make robust predictions of compliance. MDPI 2021-11-30 /pmc/articles/PMC8656817/ /pubmed/34886362 http://dx.doi.org/10.3390/ijerph182312635 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Oldroyd, Rachel A.
Morris, Michelle A.
Birkin, Mark
Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach
title Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach
title_full Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach
title_fullStr Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach
title_full_unstemmed Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach
title_short Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach
title_sort predicting food safety compliance for informed food outlet inspections: a machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8656817/
https://www.ncbi.nlm.nih.gov/pubmed/34886362
http://dx.doi.org/10.3390/ijerph182312635
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