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通过将机器学习模型应用于自动化奶制系统和其他农场管理数据来预测乳腺炎的特定农场效应
Muhammad N Dharejo1, Olivier Kashongwe2, Thomas Amon2,3
1Institute for Veterinary Epidemiology & Biostatistics, School of Veterinary Medicine, Free University of Berlin, House 21, Königsweg 57, 14163 Berlin, Germany.
Animals : an open access journal from MDPI
|October 16, 2025
概括
机器学习模型显示,使用自动化奶系统数据预测奶牛的乳腺炎具有前途. 然而,特定于农场的因素会对模型的准确性产生重大影响,因此需要为有效的群体管理量身定制的方法.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 动物科学动物科学
- 数据科学数据科学数据科学
背景情况:
- 乳腺炎在奶牛养殖中带来了重大的经济挑战.
- 早期和准确的乳腺炎检测对于群体的健康和利至关重要.
- 自动化挤奶系统 (AMS) 为潜在的预测建模生成大量数据.
研究的目的:
- 评估机器学习 (ML) 模型在预测乳腺炎方面的准确性.
- 调查农场特定因素对ML模型对乳腺炎预测性能的影响.
- 评估ML模型在不同奶牛场的通用性.
主要方法:
- 分析了来自德国四个奶牛农场 (2019-2024) 的588万个观测结果.
- 使用AMS和农场管理数据应用六个ML算法.
- 用准确度,灵敏度,特异性和AUC评估模型性能,并进行特定农场和一次性分析.
主要成果:
- 综合农场数据产生了高预测性能 (AUC 91-96%).
- 对个别农场的分析显示,内部模型的适应性很好 (AUC高达98%).
- 在交叉验证中观察到显著的绩效下降,表明概括不佳.
结论:
- 特定于农场的数据模式会影响ML模型对乳腺炎预测的准确性.
- 一个适合所有人的方法是不够的;每个农场都需要定制的ML模型.
- 将特定农场特征集成到ML模型中可以提高乳腺炎预测的准确性.
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