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基于时间序列特征的乳牛乳腺炎预测模型的研究

Rui Guo¹, Yongqiang Dai¹, Junjie Hu²

¹College of Information Science and Technology, Gansu Agricultural University, Lanzhou, China.

Frontiers in veterinary science

|May 9, 2025

概括

在乳牛中早期发现亚临床乳腺炎对于防止经济损失至关重要. 机器学习模型,特别是XGBoost,可以使用历史牛奶生产数据准确预测乳腺炎风险.

科学领域:

动物科学动物科学
兽医医学兽医医学兽医医学
数据科学数据科学数据科学

背景情况:

乳腺炎对乳制品行业构成重大经济威胁.
由于没有明显的症状,早期检测亚临床乳腺炎是具有挑战性的.
越来越多的人对食品安全和抗生素的使用越来越担心,需要非侵入性疾病检测方法.

研究的目的:

开发和评估基于机器学习的早期预警系统,用于预测奶牛乳腺炎风险.
确定用于早期乳腺炎检测的关键生产指标.
利用时间序列数据来提高预测准确度.

主要方法:

利用时间序列预测和机器学习技术对4000头奶牛的生产记录.
构建了一个多维特征集,包括原始指标值,月度变化率和统计特征.
训练并比较了六种模型:XGBoost,GBDT,SVM,KNN,后勤回归和LSTM,使用2821头母牛的数据.

主要成果:

极端梯度提升 (XGBoost) 模型实现了最高的性能,AUC为0.75和71.36%的准确性.
主要预测特征包括5月份的牛奶产量 (22.29%),脂肪百分比的标准偏差 (20.27%) 和脂肪百分比变化率 (19.87%).
沙普利添加式扩展 (SHAP) 分析证实了这些时间特征的重要性.

结论:

在XGBoost模型显示强大的潜力准确预测乳牛亚临床乳腺炎.
这种时间序列建模方法为奶牛场管理提供了有价值的早期预警系统.
这些发现支持通过数据驱动的监测主动预防乳腺炎的战略.

关键词:

在SHAP中,价值是SHAP值.在XGBoost中使用.身体细胞数量体细胞数量乳牛中的亚临床乳腺炎时间序列预测时间序列预测

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