Imada Jamie, Arango-Sabogal Juan Carlos, Bauman Cathy, Roche Steven, Kelton David
Department of Population Medicine, University of Guelph, Guelph, ON N1G 2W1, Canada.
Département de Pathologie et Microbiologie, Faculté de Médecine Vétérinaire, Université de Montréal, Saint-Hyacinthe, QC J2S 2M2, Canada.
Animals (Basel). 2024 Apr 5;14(7):1113. doi: 10.3390/ani14071113.
Machine learning algorithms have been applied to various animal husbandry and veterinary-related problems; however, its use in Johne's disease diagnosis and control is still in its infancy. The following proof-of-concept study explores the application of tree-based (decision trees and random forest) algorithms to analyze repeat milk testing data from 1197 Canadian dairy cows and the algorithms' ability to predict future Johne's test results. The random forest models using milk component testing results alongside past Johne's results demonstrated a good predictive performance for a future Johne's ELISA result with a dichotomous outcome (positive vs. negative). The final random forest model yielded a kappa of 0.626, a roc AUC of 0.915, a sensitivity of 72%, and a specificity of 98%. The positive predictive and negative predictive values were 0.81 and 0.97, respectively. The decision tree models provided an interpretable alternative to the random forest algorithms with a slight decrease in model sensitivity. The results of this research suggest a promising avenue for future targeted Johne's testing schemes. Further research is needed to validate these techniques in real-world settings and explore their incorporation in prevention and control programs.
机器学习算法已应用于各种畜牧业和兽医相关问题；然而，其在副结核病诊断和控制中的应用仍处于起步阶段。以下概念验证研究探讨了基于树的（决策树和随机森林）算法在分析1197头加拿大奶牛的重复牛奶检测数据中的应用，以及这些算法预测未来副结核病检测结果的能力。使用牛奶成分检测结果以及过去的副结核病检测结果的随机森林模型，对于二分结果（阳性与阴性）的未来副结核病酶联免疫吸附测定（ELISA）结果显示出良好的预测性能。最终的随机森林模型的kappa值为0.626，受试者工作特征曲线下面积（roc AUC）为0.915，灵敏度为72%，特异性为98%。阳性预测值和阴性预测值分别为0.81和0.97。决策树模型为随机森林算法提供了一种可解释的替代方法，但其模型灵敏度略有下降。本研究结果为未来有针对性的副结核病检测方案提供了一条有前景的途径。需要进一步研究以在实际环境中验证这些技术，并探索将其纳入预防和控制计划。
