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

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 t

SUBMITTER: Oldroyd RA 

PROVIDER: S-EPMC8656817 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature