PDF(Pubmed)
Abstract：
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.
摘要：
机器学习算法已应用于各种畜牧业和兽医相关的问题;然而，其在Johne的疾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（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测试数据中的应用，以及算法预测未来约翰测试结果的能力。使用牛奶成分测试结果以及过去的Johne\的结果的随机森林模型对未来的Johne\的ELISA结果具有良好的预测性能，并具有二分结果（阳性与负）。最终的随机森林模型产生的κ为0.626，ROCAUC为0.915，灵敏度为72％，和98%的特异性。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可解释的替代方案，但模型灵敏度略有降低。这项研究的结果为未来有针对性的约翰的测试方案提供了一个有希望的途径。需要进一步的研究来在现实世界中验证这些技术，并探索将其纳入预防和控制程序。
