Autor(es): Silva, Maria ; Faria, Brígida Mónica ; Mónica Faria, Brígida ; Reis, Luís Paulo
Data: 2023
Identificador Persistente: http://hdl.handle.net/10400.22/29897
Origem: Repositório Científico do Instituto Politécnico do Porto
Assunto(s): Food safety; Risk assessment; Public health; Knowledge discovery
Foodborne diseases continue to spread widely in the 21st century. In Portugal, the Economic and Food Safety Authority (ASAE), have the goal of monitoring and preventing non-compliance with regulatory legislation on food safety, regulating the conduct of economic activities in the food and non-food sectors, as well as accessing and communicating risks in the food chain. This work purpose and evaluated a global risk indicator considering three risk factors provided by ASAE (non-compliance rate, product or service risk and consumption volume). It also compares the performance on the prediction of risk of four classification models Decision Tree, Naïve Bayes, k-Nearest Neighbor and Artificial Neural Network before and after feature selection and hyperparameter tuning. The principal findings revealed that the service provider, food and beverage and retail were the activity sectors present in the dataset with the highest global risk associated with them. It was also observed that the Decis ion Tree classifier presented the best results. It was also verified that data balancing using the SMOTE method led to a performance increase of about 90% with the Decision Tree and k-Nearest Neighbor models. The use of machine learning can be helpful in risk assessment related to food safety and public health. It was possible to conclude that areas regarding major global risks are the ones that are more frequented by the population and require more attention. Thus, relying on risk assessment using machine learning can have a positive influence on economic crime prevention related to food safety as well as public health.
