Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants
Abstract Background Bluetongue (BT) is a disease of concern to animal breeders, so the question on their minds is whether they can predict the risk of the disease before it occurs. The main objective of this study is to enhance the accuracy of BT risk prediction by relying on machine learning (ML) a...
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| Format: | Article |
| Language: | English |
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BMC
2022-11-01
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| Series: | BMC Veterinary Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12917-022-03486-z |
| _version_ | 1811230370200289280 |
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| author | Hagar F. Gouda Fardos A. M. Hassan Eman E. El-Araby Sherif A. Moawed |
| author_facet | Hagar F. Gouda Fardos A. M. Hassan Eman E. El-Araby Sherif A. Moawed |
| author_sort | Hagar F. Gouda |
| collection | DOAJ |
| description | Abstract Background Bluetongue (BT) is a disease of concern to animal breeders, so the question on their minds is whether they can predict the risk of the disease before it occurs. The main objective of this study is to enhance the accuracy of BT risk prediction by relying on machine learning (ML) approaches to help in fulfilling this inquiry. Several risk factors of BT that affect the occurrence and magnitude of animal infection with the virus have been reported globally. Additionally, risk factors, such as sex, age, species, and season, unevenly affect animal health and welfare. Therefore, the seroprevalence study data of 233 apparently healthy animals (125 sheep and 108 goats) from five different provinces in Egypt were used to analyze and compare the performance of the algorithms in predicting BT risk. Results Logistic regression (LR), decision tree (DT), random forest (RF), and a feedforward artificial neural network (ANN) were used to develop predictive BT risk models and compare their performance to the base model (LR). Model performance was assessed by the area under the receiver operating characteristics curve (AUC), accuracy, true positive rate (TPR), false positive rate (FPR), false negative rate (FNR), precision, and F1 score. The results indicated that RF performed better than other models, with an AUC score of 81%, ANN of 79.6%, and DT of 72.85%. In terms of performance and prediction, LR showed a much lower value (AUC = 69%). Upon further observation of the results, it was discovered that age and season were the most important predictor variables reported in classification and prediction. Conclusion The findings of this study can be utilized to predict and control BT risk factors in sheep and goats, with better diagnostic discrimination in terms of accuracy, TPR, FNR, FPR, and precision of ML models over traditional and commonly used LR models. Our findings advocate that the implementation of ML algorithms, mainly RF, in farm decision making and prediction is a promising technique for analyzing cross-section studies, providing adequate predictive power and significant competence in identifying and ranking predictors representing potential risk factors for BT. |
| first_indexed | 2024-04-12T10:28:21Z |
| format | Article |
| id | doaj.art-72a9f48c39564d04a06f5eab4c510aaf |
| institution | Directory Open Access Journal |
| issn | 1746-6148 |
| language | English |
| last_indexed | 2024-04-12T10:28:21Z |
| publishDate | 2022-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Veterinary Research |
| spelling | doaj.art-72a9f48c39564d04a06f5eab4c510aaf2022-12-22T03:36:55ZengBMCBMC Veterinary Research1746-61482022-11-0118111010.1186/s12917-022-03486-zComparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminantsHagar F. Gouda0Fardos A. M. Hassan1Eman E. El-Araby2Sherif A. Moawed3Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig UniversityDepartment of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig UniversityDepartment of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig UniversityDepartment of Animal Wealth Development, Faculty of Veterinary Medicine, Suez Canal UniversityAbstract Background Bluetongue (BT) is a disease of concern to animal breeders, so the question on their minds is whether they can predict the risk of the disease before it occurs. The main objective of this study is to enhance the accuracy of BT risk prediction by relying on machine learning (ML) approaches to help in fulfilling this inquiry. Several risk factors of BT that affect the occurrence and magnitude of animal infection with the virus have been reported globally. Additionally, risk factors, such as sex, age, species, and season, unevenly affect animal health and welfare. Therefore, the seroprevalence study data of 233 apparently healthy animals (125 sheep and 108 goats) from five different provinces in Egypt were used to analyze and compare the performance of the algorithms in predicting BT risk. Results Logistic regression (LR), decision tree (DT), random forest (RF), and a feedforward artificial neural network (ANN) were used to develop predictive BT risk models and compare their performance to the base model (LR). Model performance was assessed by the area under the receiver operating characteristics curve (AUC), accuracy, true positive rate (TPR), false positive rate (FPR), false negative rate (FNR), precision, and F1 score. The results indicated that RF performed better than other models, with an AUC score of 81%, ANN of 79.6%, and DT of 72.85%. In terms of performance and prediction, LR showed a much lower value (AUC = 69%). Upon further observation of the results, it was discovered that age and season were the most important predictor variables reported in classification and prediction. Conclusion The findings of this study can be utilized to predict and control BT risk factors in sheep and goats, with better diagnostic discrimination in terms of accuracy, TPR, FNR, FPR, and precision of ML models over traditional and commonly used LR models. Our findings advocate that the implementation of ML algorithms, mainly RF, in farm decision making and prediction is a promising technique for analyzing cross-section studies, providing adequate predictive power and significant competence in identifying and ranking predictors representing potential risk factors for BT.https://doi.org/10.1186/s12917-022-03486-zRandom forestsClassificationVariable importanceMachine learningBluetongueANN |
| spellingShingle | Hagar F. Gouda Fardos A. M. Hassan Eman E. El-Araby Sherif A. Moawed Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants BMC Veterinary Research Random forests Classification Variable importance Machine learning Bluetongue ANN |
| title | Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants |
| title_full | Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants |
| title_fullStr | Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants |
| title_full_unstemmed | Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants |
| title_short | Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants |
| title_sort | comparison of machine learning models for bluetongue risk prediction a seroprevalence study on small ruminants |
| topic | Random forests Classification Variable importance Machine learning Bluetongue ANN |
| url | https://doi.org/10.1186/s12917-022-03486-z |
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