Author:
Gouda Hagar F.,Hassan Fardos A. M.,El-Araby Eman E.,Moawed Sherif A.
Abstract
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.
Publisher
Springer Science and Business Media LLC
Subject
General Veterinary,General Medicine
Reference57 articles.
1. Kyriakis CS, Billinis C, Papadopoulos E, Vasileiou NG, Athanasiou LV, Fthenakis GC. Bluetongue in small ruminants: An opinionated review, with a brief appraisal of the 2014 outbreak of the disease in Greece and the south-east Europe. Vet Microbiol. 2015;181(1–2):66–74.
2. Yavari M, Gharekhani J, Mohammadzadeh A. Bluetongue virus seropositivity and some risk factors affecting bluetongue virus infection in sheep flocks. Comp Clin Pathol. 2018;27(4):1017–22.
3. Rushton J, Lyons N. Economic impact of Bluetongue: a review of the effects on production. Veterinaria Italiana. 2015;51(4):401–6.
4. OIE. Manual of diagnostic tests and vaccines for terrestrial animals. Bluetongue (INFECTION WITH BLUETONGUE-VIRUS).CHAPTER3.1.3. https://www.oieint/fileadmin/Home/eng/Health_standards/tahm/30103_BLUETONGUEpdf 2021. Accessed 25 Feb 2022.
5. Zargoun F, Henawy IM, Ziedan NI. Effects of walls and floors in indoor localization using tracking algorithm. Int J Adv Comput Sci Appl. 2016;7(3):34–9.