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
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. H9N2 Cases and Data Collection
2.2. Xgboost Algorithm
2.3. Framework Design
2.4. Evaluation Criteria
- (1)
- Accuracy rate (ACC)
- (2)
- Recall rate (RR)
- (3)
- Precision rate (PR)
3. Results
3.1. Descriptive Statistic
3.2. Predictive Performance Comparison
3.3. Feature Importance
4. Discussion
4.1. Relationship between Environment and Production Variables and H9N2 Status
4.2. Framework Performance and Model Interpretation
4.3. Potential Applications
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variables | Variables |
---|---|
Date | Laying rate/% |
Age/days | Mortality/% |
Maximum temperature throughout the day (Tmax)/°C | Number of abnormal laying hens (Nab-hens) |
Minimum temperature throughout the day (Tmin)/°C | Qualified rate of immunization (QRimmu)/% |
Average relative humidity (RH)/% | Treatment for H9N2 infection |
Variable | Unit | Max | Mean | Min | SD | |
---|---|---|---|---|---|---|
House 1 | Age | days | 199 | - | 161 | - |
Tmax | °C | 24 | 15 | 5 | 5 | |
Tmin | °C | 9 | 1 | −6 | 4 | |
RH | % | 65 | 52 | 38 | 7 | |
Laying rate | % | 97.1 | 92.5 | 87.4 | 2.9 | |
Mortality | % | 0.10 | 0.03 | 0.01 | 0.02 | |
Nab-hens | hens | 201 | - | 0 | - | |
QRimmu | % | 90 | 90 | 90 | 0 | |
House 2 | Age | days | 258 | - | 218 | - |
Tmax | °C | 22 | 11 | 3 | 5 | |
Tmin | °C | 9 | −2 | −10 | 4 | |
RH | % | 65 | 49 | 35 | 8 | |
Laying rate | % | 94.3 | 92.4 | 86.1 | 2.3 | |
Mortality | % | 0.20 | 0.04 | 0.01 | 0.03 | |
Nab-hens | hens | 265 | - | 0 | - | |
QRimmu | % | 80 | 80 | 80 | 0 | |
House 3 | Age | days | 266 | - | 222 | - |
Tmax | °C | 12 | 4 | −4 | 5 | |
Tmin | °C | 1 | −6 | −14 | 4 | |
RH | % | 65 | 46 | 35 | 7 | |
Laying rate | % | 93.4 | 91.4 | 84.1 | 2.7 | |
Mortality | % | 0.13 | 0.05 | 0.01 | 0.03 | |
Nab-hens | hens | 214 | - | 0 | - | |
QRimmu | % | 80 | 80 | 80 | 0 |
Criteria | Predicted Future Days | Day + 0 | Day − 1 | Day − 2 | Day − 3 | Day − 4 |
---|---|---|---|---|---|---|
ACC/% | H9N2 status + 0 | 92.31 | 92.31 | 92.31 | 92.31 | 89.74 |
H9N2 status + 1 | 89.74 | 89.74 | 87.18 | 89.74 | 89.74 | |
H9N2 status + 2 | 84.62 | 84.62 | 84.62 | 84.62 | 84.62 | |
RR/% | H9N2 status + 0 | 92.31 | 92.31 | 92.31 | 92.31 | 84.62 |
H9N2 status + 1 | 92.31 | 92.31 | 84.62 | 92.31 | 92.31 | |
H9N2 status + 2 | 84.62 | 84.62 | 84.62 | 84.62 | 84.62 | |
PR/% | H9N2 status + 0 | 85.71 | 85.71 | 85.71 | 85.71 | 84.62 |
H9N2 status + 1 | 80.00 | 80.00 | 78.57 | 80.00 | 80.00 | |
H9N2 status + 2 | 73.33 | 73.33 | 73.33 | 73.33 | 73.33 |
Laying Hen House | Tmax | Tmin | RH | Laying Rate | Mortality |
---|---|---|---|---|---|
House 1 | −0.61 ** | −0.45 ** | −0.01 | −0.58 ** | 0.67 ** |
House 2 | −0.30 | −0.36 * | −0.31 * | −0.86 ** | 0.66 ** |
House 3 | −0.67 ** | −0.52 ** | −0.21 | −0.87 ** | 0.83 ** |
Predicted H9N2 Status | Warning Level | Meaning | H9N2 Development | |
---|---|---|---|---|
H9N2 Status + 0 | H9N2 Status + 1 | |||
0 | 0 | Green | Safe | None |
0 | 1 | Yellow | Low warning | Start |
1 | 1 | Red | High warning | Development |
1 | 0 | Yellow | Low warning | Nearly finish |
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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 2023, 13, 1494. https://doi.org/10.3390/ani13091494
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. 2023; 13(9):1494. https://doi.org/10.3390/ani13091494
Chicago/Turabian StyleLiu, Yu, Yanrong Zhuang, Ligen Yu, Qifeng Li, Chunjiang Zhao, Rui Meng, Jun Zhu, and Xiaoli Guo. 2023. "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 13, no. 9: 1494. https://doi.org/10.3390/ani13091494