使用机器学习自动化对兽医生物安全建议进行分类
Vitória R Lima-Campêlo1, Mariana Fonseca1, Marie-Pascale Morin1
1Département de pathologie et microbiologie, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC J2S 7C6, Canada.
Preventive veterinary medicine
|October 16, 2025
概括
开发了一个机器学习模型,自动对加拿大乳制品农场的兽医生物安全建议进行分类. 支持矢量机算法表现出强的性能,有助于高效的农场管理决策.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 根据ProAction®计划,加拿大奶农必须对生物安全风险进行评估.
- 兽医提供个性化的生物安全建议,产生大量的文本数据.
- 将这些建议的分类自动化可以提高农场管理.
研究的目的:
- 开发和评估一种机器学习模型,将兽医生物安全建议分为12个类别.
- 评估不同机器学习算法的性能,用于此分类任务.
- 为了确定模型在分类新,未见的数据中的有效性.
主要方法:
- 来自11,250个兽医建议 (2018-2021) 的文本数据被翻译成法语,以保持一致性.
- 三个算法 (多项天真贝叶斯,支持向量机,随机森林) 被训练和比较.
- 使用精度,回忆和F1得分来评估性能,并对新数据进行单独的验证 (科恩卡帕).
主要成果:
- 支持矢量机 (SVM) 算法展示了最高的性能和高效的处理.
- 经过训练的SVM模型在一个新的数据集上获得了0.88的Cohen's Kappa,这表明它与人类分类有很强的一致性.
- 该模型成功地分类了来自加拿大多种乳牛群的生物安全建议.
结论:
- 机器学习为牛奶养殖中生物安全建议的分类自动化提供了一个强大的工具.
- 这项技术可以支持及时和明智的决策,以改善群体管理和生物安全.
- 开发的SVM模型显示了在兽医和农业领域实际应用的巨大潜力.
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