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Ensemble Learning Models for Food Safety Risk Prediction

Author

Listed:
  • Li-Ya Wu

    (Division of Risk Management, Food and Drug Administration, Ministry of Welfare, Taipei 115209, Taiwan
    Department of Information and Finance Management, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Sung-Shun Weng

    (Department of Information and Finance Management, National Taipei University of Technology, Taipei 10608, Taiwan)

Abstract

Ensemble learning was adopted to design risk prediction models with the aim of improving border inspection methods for food imported into Taiwan. Specifically, we constructed a set of prediction models to enhance the hit rate of non-conforming products, thus strengthening the border control of food products to safeguard public health. Using five algorithms, we developed models to provide recommendations for the risk assessment of each imported food batch. The models were evaluated by constructing a confusion matrix to calculate predictive performance indicators, including the positive prediction value (PPV), recall, harmonic mean of PPV and recall (F1 score), and area under the curve. Our results showed that ensemble learning achieved better and more stable prediction results than any single algorithm. When the results of comparable data periods were examined, the non-conformity hit rate was found to increase significantly after online implementation of the ensemble learning models, indicating that ensemble learning was effective at risk prediction. In addition to enhancing the inspection hit rate of non-conforming food, the results of this study can serve as a reference for the improvement of existing random inspection methods, thus strengthening capabilities in food risk management.

Suggested Citation

  • Li-Ya Wu & Sung-Shun Weng, 2021. "Ensemble Learning Models for Food Safety Risk Prediction," Sustainability, MDPI, vol. 13(21), pages 1-26, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12291-:d:673991
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    References listed on IDEAS

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    1. Amirhosein Mosavi & Farzaneh Sajedi Hosseini & Bahram Choubin & Massoud Goodarzi & Adrienn A. Dineva & Elham Rafiei Sardooi, 2021. "Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 23-37, January.
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