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Article

Ensemble Learning Models for Food Safety Risk Prediction

1
Division of Risk Management, Food and Drug Administration, Ministry of Welfare, Taipei 115209, Taiwan
2
Department of Information and Finance Management, National Taipei University of Technology, Taipei 10608, Taiwan
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(21), 12291; https://doi.org/10.3390/su132112291
Submission received: 8 October 2021 / Revised: 29 October 2021 / Accepted: 2 November 2021 / Published: 7 November 2021

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.
Keywords: food safety; risk prediction; border control; ensemble learning; machine learning; bagging food safety; risk prediction; border control; ensemble learning; machine learning; bagging

Share and Cite

MDPI and ACS Style

Wu, L.-Y.; Weng, S.-S. Ensemble Learning Models for Food Safety Risk Prediction. Sustainability 2021, 13, 12291. https://doi.org/10.3390/su132112291

AMA Style

Wu L-Y, Weng S-S. Ensemble Learning Models for Food Safety Risk Prediction. Sustainability. 2021; 13(21):12291. https://doi.org/10.3390/su132112291

Chicago/Turabian Style

Wu, Li-Ya, and Sung-Shun Weng. 2021. "Ensemble Learning Models for Food Safety Risk Prediction" Sustainability 13, no. 21: 12291. https://doi.org/10.3390/su132112291

APA Style

Wu, L.-Y., & Weng, S.-S. (2021). Ensemble Learning Models for Food Safety Risk Prediction. Sustainability, 13(21), 12291. https://doi.org/10.3390/su132112291

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