Automated prediction of mastitis infection patterns in dairy herds using machine learning

Hyde, Robert; Down, Peter; Bradley, Andrew J.; Breen, James; Hudson, Chris; Leach, K. A.; Green, Martin J.

doi:10.1038/s41598-020-61126-8

Cited by 44 publications

(27 citation statements)

References 35 publications

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“…In addition, similar performance of investigated methods on both testing and validation sets indicated the absence of overfitting. These results suggested the possibility to apply findings of this study also to other dairy herds 22 .…”

Section: Discussionsupporting

confidence: 57%

Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows

Bobbo

Biffani

Taccioli

et al. 2021

Sci Rep

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Bovine mastitis is one of the most important economic and health issues in dairy farms. Data collection during routine recording procedures and access to large datasets have shed the light on the possibility to use trained machine learning algorithms to predict the udder health status of cows. In this study, we compared eight different machine learning methods (Linear Discriminant Analysis, Generalized Linear Model with logit link function, Naïve Bayes, Classification and Regression Trees, k-Nearest Neighbors, Support Vector Machines, Random Forest and Neural Network) to predict udder health status of cows based on somatic cell counts. Prediction accuracies of all methods were above 75%. According to different metrics, Neural Network, Random Forest and linear methods had the best performance in predicting udder health classes at a given test-day (healthy or mastitic according to somatic cell count below or above a predefined threshold of 200,000 cells/mL) based on the cow’s milk traits recorded at previous test-day. Our findings suggest machine learning algorithms as a promising tool to improve decision making for farmers. Machine learning analysis would improve the surveillance methods and help farmers to identify in advance those cows that would possibly have high somatic cell count in the subsequent test-day.

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Section: Discussionsupporting

confidence: 57%

Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows

Bobbo

Biffani

Taccioli

et al. 2021

Sci Rep

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“…The sample size included in this study is comparable to similar studies. However it is possible that additional training examples would support further improvements in prediction 13 , 16 . Finally, further investigation on an independent dataset from a different cohort of dogs could examine the external validation of these models 40 .…”

Section: Discussionmentioning

confidence: 99%

Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice

Schofield

Brodbelt

Kennedy

et al. 2021

Sci Rep

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Cushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs.

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“…ANN, according to several authors [3][4][5][6][7][8][9][11][12][13][14][15], is a promising tool in analyzing and predicting lots of complex issues that exist in animal studies. As indicated by Fernández et al [10], its advantage over traditional analytical methods is due to its accuracy of estimation as well as its ability to generalize even when less significant data is entered.…”

Section: Discussionmentioning

confidence: 99%

“…The application of artificial neural networks (ANN) has already become commonplace in modelling as well as in the optimization of several processes in technical studies [1,2]. It is also increasingly becoming a subject of interest to scientists engaged in issues concerning animal breeding and use and optimal nutrition [3][4][5][6][7][8]. This has been due, partly though, to the need to seek alternative methods with high potentials of application and offer reliable information (data) that enhances forecasting.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial neural networks have found their use in several fields of animal husbandry. The designing of an appropriate model that takes account of measurable and immeasurable features offers possibilities for, amongst others, forecasting the occurrence of lameness in horses [3], assessing animal behaviors while estimating their levels of welfare [9], analyzing the factors impacting on milk yield in cows [4] and goats [10], susceptibility to mastitis in cattle [6,11] as well as the risk of occurrence of complications in parturition [12], predicting the occurrence of African horse sickness [13], in genomic selection in cattle [14], and including research in the field of evolution [15]. Data analysis with the use of artificial neural networks would pave the way for precise classification and adjustments to the expected pattern, clustering, modelling, and forecasting.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Topczewska

Kwater

2020

Sustainability

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The paper suggests the application of artificial neural networks (ANN) for the analysis of variables that significantly impact on the results of Hucul horses that participate at the National Breeding and Utility Championships for Hucul horses. The study exploits the results obtained during 2009–2015. The research material collected enabled the creation of a set of input data (for the artificial neural network), out of which independent learning and testing sets were isolated. The neural classification system in form of a multi-layered artificial neural network suggested in this paper was implemented in the programming environment Matlab, the 8.1.0.604 version. Each horse was described using features in three models. Experimental simulations were carried out separately for each model, conducting the learning and testing simulation process 10 times. In accepting the division of the evaluated group of horses into 10 classes for the analysis of the issue both the expert and network designated the classes, not without reservations due to imprecision of demarcations. The increase in class numbers would result in increased accuracy of selection (allocation to varied classes) of individuals. The average for 10 network responses which was 77% suggest an identical or a very similar horse class when compared with the expert’s value. Preliminary results of the application of artificial neural networks in predicting the utility value of Hucul horses, relying on a specific set of features seem promising.

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Automated prediction of mastitis infection patterns in dairy herds using machine learning

Cited by 44 publications

References 35 publications

Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows

Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows

Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice

Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

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