Asgari Zeinab, Sadeghi-Sefidmazgi Ali, Pakdel Abbas, Shahinfar Saleh
Department of Animal Sciences, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran.
Department of Animal Science, University of Tehran, Karaj 3158711167-4111, Iran.
Animals (Basel). 2025 Jun 20;15(13):1833. doi: 10.3390/ani15131833.
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.
真胃移位（DA）是一种消化系统疾病，会因奶牛产奶量下降和过早淘汰而导致严重的经济损失。对易患DA的病例进行预测对于减少经济损失至关重要。本研究旨在对DA进行早期预测。然而，识别有DA风险的奶牛可能很困难，因为DA是一个复杂的性状，其发病率较低。为此，在本研究中，研究了逻辑回归（LR）、朴素贝叶斯（NB）、决策树、随机森林（RF）和梯度提升机（GBM）这五种机器学习算法预测DA病例的能力。对于这些预测，可获得来自2010年至2020年间产犊的7个荷斯坦奶牛群的20个特定牛群-奶牛特征和父系遗传信息。模型性能指标表明，GBM和RF算法在预测DA方面表现优于其他算法，F2度量为0.32。与其他方法相比，RF中的真阳性率最高，为0.75，其次是GBM，为0.70。考虑到数据高度不平衡，本研究显示了预测易患DA病例的潜力。这种预测工具可以帮助奶农通过识别易患DA的奶牛来做出预防性管理决策。
