离散的同质和异质马尔科夫链增强乳牛疾病的预测建模
Jan Saro1, Jaromir Ducháček2, Helena Brožová1
1Department of Systems Engineering, Faculty of Economics and Management, Czech University of Life Sciences Prague, Kamycka 129, Suchdol, 165 00 Prague, Czech Republic.
Animals : an open access journal from MDPI
|September 14, 2024
概括
这项研究引入了一种新的马尔科夫链模型,用于预测奶牛疾病,改善群体健康管理. 该模型在有限的农场数据上提供准确的预测,降低了农民的成本.
科学领域:
- 兽医流行病学 兽医流行病学
- 农业技术 农业技术
- 数据科学是数据科学.
背景情况:
- 预测乳牛疾病对于群体健康管理和经济可行性至关重要.
- 现有的机器学习模型面临的局限性是由于疾病特异性发展和稀缺的农场数据.
研究的目的:
- 使用马尔科夫链开发一种新的乳牛疾病预测模型.
- 解决机器学习方法中固有的数据局限性,用于疾病预测.
主要方法:
- 用于疾病建模的离散均和非均的马尔科夫链.
- 开发了一种方法来确定马尔科夫链状态的最佳数量.
- 采用了切比舍夫距离最小化来选择最好的预测模型.
主要成果:
- 在19种疾病中的14种疾病中,实际和预测数据之间的最大差异低于15%.
- 展示了一个可适应低技术奶牛场实施的模型.
- 展示了扩展到其他疾病类型的潜力,最小的调整.
结论:
- 马尔科夫链模型为乳牛疾病预测提供了有效的解决方案,克服了数据稀缺问题.
- 这种模式可以增强决策支持系统,从而改善群体健康和基于证据的农业战略.
- 促进乳制品养殖中抗生素等治疗方法的成本预测.
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