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

Rachel A Oldroyd,Mark Birkin,Michelle A Morris

doi:10.3390/ijerph182312635

Rachel A Oldroyd, Mark Birkin + Show 1 more

Open Access

https://doi.org/10.3390/ijerph182312635

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Abstract

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.

Highlights

Patterns of national food consumption have changed dramatically in recent years.In the most recent wave of the Food and You survey, it was reported that 98% of the UK population consume food from takeaways and restaurants, and at least 43% do so on a weekly basis [1]
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
Socio-demographic, business type, and urbanness data at the output area (OA) level were used to train the models. These include age data, ethnicity data, deprivation data, region, rural urban classification, and output area classification. This approach is novel; we reviewed literature which has identified associations between neighborhood characteristics and food hygiene compliance

Summary

Introduction

Patterns of national food consumption have changed dramatically in recent years. In the most recent wave of the Food and You survey, it was reported that 98% of the UK population consume food from takeaways and restaurants, and at least 43% do so on a weekly basis [1]. With consumers eating fewer home cooked meals than ever before, the governance of food-serving businesses is increasingly important, especially considering that an estimated 60% [3] of 2.4 million annual cases of foodborne disease [4] are thought to be contracted whilst eating away from home. Overseen by the Food Standards Agency (FSA), local and unitary authorities (referred to as LA’s for the remainder of this paper) are responsible for enforcing hygiene standards within food businesses in the UK.

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