使用机器学习来改善牛群水平爆发的牛结核病控制
概括
机器学习模型,特别是随机森林,可以改善牛的牛结核病 (bTB) 检测. 这加强了监测,减少了疫情持续时间,有助于国家控制计划.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 动物健康 动物健康
- 机器学习在流行病学中的应用
背景情况:
- 牛结核病 (bTB) 造成了重大经济损失,而且缺乏确定的死前诊断测试.
- 目前的控制措施包括结核素皮肤测试 (CICT) 和干扰素玛 (IFN-γ) 试验,但这些有局限性.
研究的目的:
- 开发和评估机器学习模型,用于预测牛群真正的结核病感染.
- 加强现有的BTB监督计划的质量保证.
主要方法:
- 在CICT之后,利用了牛反应器中结核素回归的数据.
- 应用机器学习技术,包括决策树,包装树和随机森林与平衡方法 (例如,SMOTE).
主要成果:
- 使用SMOTE平衡训练的随机森林 (RF) 获得了最高的精度 (0.90).
- 该模型确定了IFN-γ测定组件的重要性,并建议对大型疫情进行值调整.
- 结合的RF和IFN-γ模型显示了在分解群中改善感染检测的潜力.
结论:
- 机器学习模型,特别是射频,可以显著提高bTB检测的准确性.
- 这些模型可以加强监测,减少疫情的规模和持续时间,并支持国家疾病控制计划.
- 该方法提供了一种质量保证现有的bTB监测策略的机制.
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