Machine learning approaches for the prediction of lameness in dairy cows

Lameness is one of the costliest health problems, as well as a welfare concern in dairy cows. However, it is difficult to detect cows with possible lameness, or the ones that are at risk of becoming lame e.g. in the next week or so. In this study, we investigated the ability of three machine learnin...

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Autores principales:	S. Shahinfar, M. Khansefid, M. Haile-Mariam, J.E. Pryce
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Animal welfare Logistic regression Naïve bayes Neural networks Precision dairy farming Animal culture SF1-1100
Acceso en línea:	https://doaj.org/article/4b6459488cac4416b0d58dcc2864e71f
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spelling	oai:doaj.org-article:4b6459488cac4416b0d58dcc2864e71f2021-11-26T04:25:31ZMachine learning approaches for the prediction of lameness in dairy cows1751-731110.1016/j.animal.2021.100391https://doaj.org/article/4b6459488cac4416b0d58dcc2864e71f2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1751731121002342https://doaj.org/toc/1751-7311Lameness is one of the costliest health problems, as well as a welfare concern in dairy cows. However, it is difficult to detect cows with possible lameness, or the ones that are at risk of becoming lame e.g. in the next week or so. In this study, we investigated the ability of three machine learning algorithms, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP), to predict cases of lameness using milk production and conformation traits. The performance of these algorithms was compared with logistic regression (LR) as the gold standard approach for binary classification. We had a total of 2 535 lameness scores (2 248 sound and 287 unsound) and 29 predictor features from nine dairy herds in Australia to predict lameness incidence. Training was done on 80% of the data within each herd with the remainder used as validation set. Our results indicated that in terms of area under curve of receiver operating characteristics, there were negligible differences between LR (0.67) and NB (0.66) while MLP (0.62) and RF (0.61) underperformed compared to the other two methods. However, the F1-score in NB (27%) outperformed LR (1%), suggesting that NB could potentially be a more reliable method for the prediction of lameness in practice, given enough relevant data are available for proper training, which was a limitation in this study. Considering the small size of our dataset, lack of information about environmental conditions prior to the incidence of lameness, management practices, short time gap between production records and lameness scoring, and farm information, this study proved the concept of using machine learning predictive models to predict the incidence of lameness a priori to its occurrence and thus may become a valuable decision support system for better lameness management in precision dairy farming.S. ShahinfarM. KhansefidM. Haile-MariamJ.E. PryceElsevierarticleAnimal welfareLogistic regressionNaïve bayesNeural networksPrecision dairy farmingAnimal cultureSF1-1100ENAnimal, Vol 15, Iss 11, Pp 100391- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Animal welfare Logistic regression Naïve bayes Neural networks Precision dairy farming Animal culture SF1-1100
spellingShingle	Animal welfare Logistic regression Naïve bayes Neural networks Precision dairy farming Animal culture SF1-1100 S. Shahinfar M. Khansefid M. Haile-Mariam J.E. Pryce Machine learning approaches for the prediction of lameness in dairy cows
description	Lameness is one of the costliest health problems, as well as a welfare concern in dairy cows. However, it is difficult to detect cows with possible lameness, or the ones that are at risk of becoming lame e.g. in the next week or so. In this study, we investigated the ability of three machine learning algorithms, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP), to predict cases of lameness using milk production and conformation traits. The performance of these algorithms was compared with logistic regression (LR) as the gold standard approach for binary classification. We had a total of 2 535 lameness scores (2 248 sound and 287 unsound) and 29 predictor features from nine dairy herds in Australia to predict lameness incidence. Training was done on 80% of the data within each herd with the remainder used as validation set. Our results indicated that in terms of area under curve of receiver operating characteristics, there were negligible differences between LR (0.67) and NB (0.66) while MLP (0.62) and RF (0.61) underperformed compared to the other two methods. However, the F1-score in NB (27%) outperformed LR (1%), suggesting that NB could potentially be a more reliable method for the prediction of lameness in practice, given enough relevant data are available for proper training, which was a limitation in this study. Considering the small size of our dataset, lack of information about environmental conditions prior to the incidence of lameness, management practices, short time gap between production records and lameness scoring, and farm information, this study proved the concept of using machine learning predictive models to predict the incidence of lameness a priori to its occurrence and thus may become a valuable decision support system for better lameness management in precision dairy farming.
format	article
author	S. Shahinfar M. Khansefid M. Haile-Mariam J.E. Pryce
author_facet	S. Shahinfar M. Khansefid M. Haile-Mariam J.E. Pryce
author_sort	S. Shahinfar
title	Machine learning approaches for the prediction of lameness in dairy cows
title_short	Machine learning approaches for the prediction of lameness in dairy cows
title_full	Machine learning approaches for the prediction of lameness in dairy cows
title_fullStr	Machine learning approaches for the prediction of lameness in dairy cows
title_full_unstemmed	Machine learning approaches for the prediction of lameness in dairy cows
title_sort	machine learning approaches for the prediction of lameness in dairy cows
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/4b6459488cac4416b0d58dcc2864e71f
work_keys_str_mv	AT sshahinfar machinelearningapproachesforthepredictionoflamenessindairycows AT mkhansefid machinelearningapproachesforthepredictionoflamenessindairycows AT mhailemariam machinelearningapproachesforthepredictionoflamenessindairycows AT jepryce machinelearningapproachesforthepredictionoflamenessindairycows
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Machine learning approaches for the prediction of lameness in dairy cows

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