Food Safety Risk Prediction and Regulatory Policy Implications Based on Machine Learning:Evidence from Fresh Aquatic Products
Accurate identification and prediction of food safety risks are crucial for enhancing mod-ern food safety assurance capabilities.In light of the complex nature of food safety risks and the ineffi-ciencies in regulatory processes,establishing a dependable early warning mechanism can help the Chi-nese government overcome the challenge of excessive investment in regulatory resources with low effi-ciency.This paper utilizes nationwide sampling data encompassing over 300,000 fresh aquatic products from 2014 to 2022 to construct a machine learning prediction model for assessing risks associated with fresh aquatic products.Various prediction features are sorted according to the importance of risk causes,and the impact of important features on the risks of fresh aquatic products is analyzed using econometric methods.The findings demonstrate that the random forest model achieves a sensitivity and accuracy rate of 75.4％and 78.0％respectively,in predicting risks linked to fresh aquatic products.These risks are closely intertwined with five key dimensions:supply chain links,regions,government supervision intensi-ty,aquatic product categories,and weather conditions.This paper provides a new perspective for accurate prediction and cause analysis of food safety risks in China,and also provides reliable evidence for optimizing the allocation of government food safety supervision resources.
NETL
NSTL
万方数据
