Data-Driven Prediction of Foodborne Disease Pathogens

Xiang Chen, Hongzhi Wang

Abstract: In recent years, foodborne diseases have become one of the most Data analysis technology has been widely used in the field of public health, and greatly facilitates the preliminary judgment of medical staff. Foodborne pathogens, as the main factor of foodborne diseases, play an important role in the treatment and prevention of foodborne diseases. However, foodborne diseases caused by different pathogens lack specificity in clinical features, and the actual clinical pathogen detection ratio is very low in reality. This paper proposes a data-driven foodborne disease pathogen prediction model, which paves the way for early and effective patient identification and treatment. Data analysis was implemented to model the foodborne disease case data. The best model achieves good classification accuracy for Salmonella, Norovirus, Vibrio parahaemolyticus, Staphylococcus aureus, Shigella and Escherichia coli. With the patient data input, the model can conduct rapid risk assessment. The experimental results show that the data-driven approach reduces manual intervention and the difficulty of testing.