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Classification of porcine reproductive and respiratory syndrome clinical impact in Ontario sow herds using machine learning approaches

Since the early 1990s, porcine reproductive and respiratory syndrome (PRRS) virus outbreaks have been reported across various parts of North America, Europe, and Asia. The incursion of PRRS virus (PRRSV) in swine herds could result in various clinical manifestations, resulting in a substantial impac...

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Autores principales: Chadha, Akshay, Dara, Rozita, Pearl, David L., Gillis, Daniel, Rosendal, Thomas, Poljak, Zvonimir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10284593/
https://www.ncbi.nlm.nih.gov/pubmed/37351555
http://dx.doi.org/10.3389/fvets.2023.1175569
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author Chadha, Akshay
Dara, Rozita
Pearl, David L.
Gillis, Daniel
Rosendal, Thomas
Poljak, Zvonimir
author_facet Chadha, Akshay
Dara, Rozita
Pearl, David L.
Gillis, Daniel
Rosendal, Thomas
Poljak, Zvonimir
author_sort Chadha, Akshay
collection PubMed
description Since the early 1990s, porcine reproductive and respiratory syndrome (PRRS) virus outbreaks have been reported across various parts of North America, Europe, and Asia. The incursion of PRRS virus (PRRSV) in swine herds could result in various clinical manifestations, resulting in a substantial impact on the incidence of respiratory morbidity, reproductive loss, and mortality. Veterinary experts, among others, regularly analyze the PRRSV open reading frame-5 (ORF-5) for prognostic purposes to assess the risk of severe clinical outcomes. In this study, we explored if predictive modeling techniques could be used to identify the severity of typical clinical signs observed during PRRS outbreaks in sow herds. Our study aimed to evaluate four baseline machine learning (ML) algorithms: logistic regression (LR) with ridge and lasso regularization techniques, random forest (RF), k-nearest neighbor (KNN), and support vector machine (SVM), for the clinical impact classification of ORF-5 sequences and demographic data into high impact and low impact categories. First, baseline classifiers were evaluated using different input representations of ORF-5 nucleotides, amino acid sequences, and demographic data using a 10-fold cross-validation technique. Then, we designed a consensus voting ensemble approach to aggregate the different types of input representations for genetic and demographic data for classifying clinical impact. In this study, we observed that: (a) for abortion and pre-weaning mortality (PWM), different classifiers gained improvement over baseline accuracy, which showed the plausible presence of both genotypic-phenotypic and demographic-phenotypic relationships, (b) for sow mortality (SM), no baseline classifier successfully established such linkages using either genetic or demographic input data, (c) baseline classifiers showed good performance with a moderate variance of the performance metrics, due to high-class overlap and the small dataset size used for training, and (d) the use of consensus voting ensemble techniques helped to make the predictions more robust and stabilized the performance evaluation metrics, but overall accuracy did not substantially improve the diagnostic metrics over baseline classifiers.
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spelling pubmed-102845932023-06-22 Classification of porcine reproductive and respiratory syndrome clinical impact in Ontario sow herds using machine learning approaches Chadha, Akshay Dara, Rozita Pearl, David L. Gillis, Daniel Rosendal, Thomas Poljak, Zvonimir Front Vet Sci Veterinary Science Since the early 1990s, porcine reproductive and respiratory syndrome (PRRS) virus outbreaks have been reported across various parts of North America, Europe, and Asia. The incursion of PRRS virus (PRRSV) in swine herds could result in various clinical manifestations, resulting in a substantial impact on the incidence of respiratory morbidity, reproductive loss, and mortality. Veterinary experts, among others, regularly analyze the PRRSV open reading frame-5 (ORF-5) for prognostic purposes to assess the risk of severe clinical outcomes. In this study, we explored if predictive modeling techniques could be used to identify the severity of typical clinical signs observed during PRRS outbreaks in sow herds. Our study aimed to evaluate four baseline machine learning (ML) algorithms: logistic regression (LR) with ridge and lasso regularization techniques, random forest (RF), k-nearest neighbor (KNN), and support vector machine (SVM), for the clinical impact classification of ORF-5 sequences and demographic data into high impact and low impact categories. First, baseline classifiers were evaluated using different input representations of ORF-5 nucleotides, amino acid sequences, and demographic data using a 10-fold cross-validation technique. Then, we designed a consensus voting ensemble approach to aggregate the different types of input representations for genetic and demographic data for classifying clinical impact. In this study, we observed that: (a) for abortion and pre-weaning mortality (PWM), different classifiers gained improvement over baseline accuracy, which showed the plausible presence of both genotypic-phenotypic and demographic-phenotypic relationships, (b) for sow mortality (SM), no baseline classifier successfully established such linkages using either genetic or demographic input data, (c) baseline classifiers showed good performance with a moderate variance of the performance metrics, due to high-class overlap and the small dataset size used for training, and (d) the use of consensus voting ensemble techniques helped to make the predictions more robust and stabilized the performance evaluation metrics, but overall accuracy did not substantially improve the diagnostic metrics over baseline classifiers. Frontiers Media S.A. 2023-06-07 /pmc/articles/PMC10284593/ /pubmed/37351555 http://dx.doi.org/10.3389/fvets.2023.1175569 Text en Copyright © 2023 Chadha, Dara, Pearl, Gillis, Rosendal and Poljak. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Veterinary Science
Chadha, Akshay
Dara, Rozita
Pearl, David L.
Gillis, Daniel
Rosendal, Thomas
Poljak, Zvonimir
Classification of porcine reproductive and respiratory syndrome clinical impact in Ontario sow herds using machine learning approaches
title Classification of porcine reproductive and respiratory syndrome clinical impact in Ontario sow herds using machine learning approaches
title_full Classification of porcine reproductive and respiratory syndrome clinical impact in Ontario sow herds using machine learning approaches
title_fullStr Classification of porcine reproductive and respiratory syndrome clinical impact in Ontario sow herds using machine learning approaches
title_full_unstemmed Classification of porcine reproductive and respiratory syndrome clinical impact in Ontario sow herds using machine learning approaches
title_short Classification of porcine reproductive and respiratory syndrome clinical impact in Ontario sow herds using machine learning approaches
title_sort classification of porcine reproductive and respiratory syndrome clinical impact in ontario sow herds using machine learning approaches
topic Veterinary Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10284593/
https://www.ncbi.nlm.nih.gov/pubmed/37351555
http://dx.doi.org/10.3389/fvets.2023.1175569
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