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9699 and 0.9582, respectively. In terms of model validation, the model provides a correct judgment of real outbreaks. In the feature importance analysis with the XGBoost model, the health status of the other people with the same exposure has the highest weight, reaching 0.65. The machine learning model built in this study exhibits high accuracy in recognizing foodborne disease outbreaks, thus reducing the manual burden for medical staff. The model helped us identify the confounding factors of foodborne disease outbreaks. Attention should b
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