Heterogeneous graph attention network for food safety risk prediction

Abstract

Food risk analysis technique is of great significance in human health and safety. This paper investigates the issue of food safety risk identification and early warning based on heterogeneous graph attention network. First, the selection of food safety risk heterogeneous indicators (such as food category, hazard, region, etc.) is analyzed, since they are the effective factors to describe risk levels, then a food safety risk assessment index system is constructed based on these factors. Second, a heterogeneous graph attention network is proposed to fuse the heterogeneous risk indicators, resulting in the novel risk profile of food safety. Finally, the proposed heterogeneous graph attention network is implemented to the problem of food safety risk early warning, which could provide some basis of risk management decision. A data set of food detection in China is used to verify the proposed method, the case study results suggest that the proposed risk profile is a valuable way to identify the food safety risk. • The main risk factors affecting food safety are analyzed, and a food safety risk evaluation index system is established. • Heterogeneous graph attention network is used to train heterogeneous data and establish food safety risk reasoning model. • The model can accurately predict the risk level of various kinds of food.