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August 18, 2025FoodsOpen Access

Identifying Key Drivers of Foodborne Diseases in Zhejiang, China: A Machine Learning Approach

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Machine learning reveals key predictors of foodborne diseases in Zhejiang, suggesting targeted public health actions.

Foodborne diseases increased complexity in Zhejiang, with an identified 41 critical predictors influencing the risk.
Random Forest and XGBoost models achieved classification accuracies of 86% and 87%, highlighting predictive power.
Temporal analysis shows important shifts in pathogens; Vibrio parahaemolyticus declined while Salmonella increased post-2016.
Seasonal patterns proved crucial for bacterial incidence, underlining the need for adaptive public health strategies to mitigate risks.

Jin et al. (2025) studied this question.

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