您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

A Machine Learning Framework Based on Extreme Gradient Boosting to Predict the Occurrence and Development of Infectious Diseases in Laying Hen Farms, Taking H9N2 as an Example

文献类型： 外文期刊

作者： Liu, Yu 1 ; Zhuang, Yanrong 3 ; Yu, Ligen 1 ; Li, Qifeng 1 ; Zhao, Chunjiang 1 ; Meng, Rui 1 ; Zhu, Jun 1 ; Guo, Xiaoli 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China

2.Natl Innovat Ctr Digital Technol Anim Husb, Beijing 100097, Peoples R China

3.China Agr Univ, Coll Water Resources & Civil Engn, Beijing 100083, Peoples R China

关键词： H9N2 status; laying hen house; machine learning; predictive performance

期刊名称：ANIMALS （ 影响因子：3.0； 五年影响因子：3.2 ）

ISSN： 2076-2615

年卷期： 2023 年 13 卷 9 期

页码：

收录情况： SCI

摘要： The H9N2 avian influenza virus has become one of the dominant subtypes of avian influenza virus in poultry and has been significantly harmful to chickens in China, with great economic losses in terms of reduced egg production or high mortality by co-infection with other pathogens. A prediction of H9N2 status based on easily available production data with high accuracy would be important and essential to prevent and control H9N2 outbreaks in advance. This study developed a machine learning framework based on the XGBoost classification algorithm using 3 months' laying rates and mortalities collected from three H9N2-infected laying hen houses with complete onset cycles. A framework was developed to automatically predict the H9N2 status of individual house for future 3 days (H9N2 status + 0, H9N2 status + 1, H9N2 status + 2) with five time frames (day + 0, day - 1, day - 2, day - 3, day - 4). It had been proven that a high accuracy rate > 90%, a recall rate > 90%, a precision rate of >80%, and an area under the curve of the receiver operator characteristic = 0.85 could be achieved with the prediction models. Models with day + 0 and day - 1 were highly recommended to predict H9N2 status + 0 and H9N2 status + 1 for the direct or auxiliary monitoring of its occurrence and development. Such a framework could provide new insights into predicting H9N2 outbreaks, and other practical potential applications to assist in disease monitor were also considerable.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台