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Liu Y, Zhuang Y, Yu L, Li Q, Zhao C, Meng R, Zhu J, Guo X. 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. Animals (Basel) 2023; 13:1494. [PMID: 37174531 PMCID: PMC10177545 DOI: 10.3390/ani13091494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
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.
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Affiliation(s)
- Yu Liu
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
| | - Yanrong Zhuang
- College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
| | - Ligen Yu
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
| | - Qifeng Li
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
| | - Chunjiang Zhao
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
| | - Rui Meng
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
| | - Jun Zhu
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
| | - Xiaoli Guo
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
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