Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows.
Sci Rep
; 11(1): 13642, 2021 07 01.
Article
en En
| MEDLINE
| ID: mdl-34211046
ABSTRACT
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.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Bovinos
/
Industria Lechera
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Aprendizaje Automático
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Mastitis Bovina
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
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Risk_factors_studies
Límite:
Animals
Idioma:
En
Revista:
Sci Rep
Año:
2021
Tipo del documento:
Article
País de afiliación:
Italia