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Machine Learning Approaches for the Prediction of Displaced Abomasum in Dairy Cows Using a Highly Imbalanced Dataset
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Department of Animal Sciences, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran
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Department of Animal Science, University of Tehran, Karaj 3158711167-4111, Iran
3
Department of Health and Aged Care, Canberra, ACT 2606, Australia
*
Author to whom correspondence should be addressed.
Animals 2025, 15(13), 1833; https://doi.org/10.3390/ani15131833
Submission received: 14 April 2025
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Revised: 1 June 2025
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Accepted: 4 June 2025
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Published: 20 June 2025
(This article belongs to the Special Issue Animal Production in the Artificial Intelligence Era: Advances and Applications)
Simple Summary
Displaced abomasum (DA) is one of the costliest health problems as well as a welfare concern in dairy cows. Therefore, the predictive potential of DA-susceptible cases is of great importance to reduce economic losses. Hence, this study aimed for early prediction of DA by using five machine learning algorithms, namely Logistic Regression (LR), Naïve Bayes (NB), Decision Tree, Random Forest (RF) and Gradient Boosting Machines (GBM), to predict cases of DA. Model performance metrics indicated that among the algorithms considered in this study, RF and GBM were significantly better in terms of F2 (0.32) and TPR (0.75 and 0.70, respectively). Therefore, given the highly unbalanced data and that DA is a complex feature, machine learning (ML) methods were shown to be promising for predicting cases susceptible to DA at the herd level. This prediction tool can aid dairy farmers in making preventative management decisions by identifying cows susceptible to DA.
Abstract
Displaced abomasum (DA) is a digestive disorder that causes severe economic losses through the reduction in milk yield and early culling of cows. The predictive potential of DA-susceptible cases is of great importance to reduce economic losses. This study aimed for early prediction of DA. However, identifying cows at risk of DA can be difficult because DA is a complex trait and its incidence is low. For this purpose, in this study, the ability of five machine learning algorithms, namely Logistic Regression (LR), Naïve Bayes (NB), Decision Tree, Random Forest (RF) and Gradient Boosting Machines (GBM), to predict cases of DA was investigated. For these predictions, 20 herd–cow-specific features and sire genetic information from 7 Holstein dairy herds that calved between 2010 and 2020 were available. Model performance metrics indicated that GBM and RF algorithms outperformed the others in predicting DA with F2 measures of 0.32. The true positive rate in the RF was the highest compared to other methods at 0.75, followed by GBM at 0.70. Given the highly imbalanced data, this study showed the potential in forecasting cases susceptible to DA. This prediction tool can aid dairy farmers in making preventative management decisions by identifying cows susceptible to DA.
