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Article

A Machine Learning Model for Food Source Attribution of Listeria monocytogenes

1
Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA
2
Center for Food Safety and Security Systems, University of Maryland, College Park, MD 20742, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Patrick Njage
Pathogens 2022, 11(6), 691; https://doi.org/10.3390/pathogens11060691
Received: 7 April 2022 / Revised: 6 June 2022 / Accepted: 10 June 2022 / Published: 16 June 2022
(This article belongs to the Special Issue Genomic Epidemiology of Foodborne Pathogens)
Despite its low morbidity, listeriosis has a high mortality rate due to the severity of its clinical manifestations. The source of human listeriosis is often unclear. In this study, we investigate the ability of machine learning to predict the food source from which clinical Listeria monocytogenes isolates originated. Four machine learning classification algorithms were trained on core genome multilocus sequence typing data of 1212 L. monocytogenes isolates from various food sources. The average accuracies of random forest, support vector machine radial kernel, stochastic gradient boosting, and logit boost were found to be 0.72, 0.61, 0.7, and 0.73, respectively. Logit boost showed the best performance and was used in model testing on 154 L. monocytogenes clinical isolates. The model attributed 17.5 % of human clinical cases to dairy, 32.5% to fruits, 14.3% to leafy greens, 9.7% to meat, 4.6% to poultry, and 18.8% to vegetables. The final model also provided us with genetic features that were predictive of specific sources. Thus, this combination of genomic data and machine learning-based models can greatly enhance our ability to track L. monocytogenes from different food sources. View Full-Text
Keywords: Listeria monocytogenes; food source attribution; whole-genome sequencing; machine learning; predictive modeling Listeria monocytogenes; food source attribution; whole-genome sequencing; machine learning; predictive modeling
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MDPI and ACS Style

Tanui, C.K.; Benefo, E.O.; Karanth, S.; Pradhan, A.K. A Machine Learning Model for Food Source Attribution of Listeria monocytogenes. Pathogens 2022, 11, 691. https://doi.org/10.3390/pathogens11060691

AMA Style

Tanui CK, Benefo EO, Karanth S, Pradhan AK. A Machine Learning Model for Food Source Attribution of Listeria monocytogenes. Pathogens. 2022; 11(6):691. https://doi.org/10.3390/pathogens11060691

Chicago/Turabian Style

Tanui, Collins K., Edmund O. Benefo, Shraddha Karanth, and Abani K. Pradhan. 2022. "A Machine Learning Model for Food Source Attribution of Listeria monocytogenes" Pathogens 11, no. 6: 691. https://doi.org/10.3390/pathogens11060691

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