Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Zagazig, 44511, Egypt.
Department of Animal Wealth Development, Faculty of Veterinary Medicine, Suez Canal University, Ismailia, 41522, Egypt.
BMC Vet Res. 2022 Nov 9;18(1):394. doi: 10.1186/s12917-022-03486-z.
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.
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.
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.
蓝舌病（BT）是动物饲养者关注的疾病，因此他们关心的问题是在疾病发生之前是否能够预测其风险。本研究的主要目的是通过依赖机器学习（ML）方法来提高 BT 风险预测的准确性，以帮助回答这一问题。已经在全球范围内报告了影响动物感染病毒的发生和严重程度的几种 BT 风险因素。此外，性别、年龄、物种和季节等风险因素会对动物的健康和福利产生不均衡的影响。因此，使用来自埃及五个不同省份的 233 只看似健康的动物（125 只绵羊和 108 只山羊）的血清流行率研究数据来分析和比较算法在预测 BT 风险方面的性能。
使用逻辑回归（LR）、决策树（DT）、随机森林（RF）和前馈人工神经网络（ANN）来开发预测性 BT 风险模型，并将其性能与基础模型（LR）进行比较。通过接收者操作特征曲线（AUC）下的面积、准确性、真阳性率（TPR）、假阳性率（FPR）、假阴性率（FNR）、精度和 F1 评分来评估模型性能。结果表明，RF 的性能优于其他模型，AUC 评分为 81%，ANN 为 79.6%，DT 为 72.85%。就性能和预测而言，LR 的值低得多（AUC=69%）。进一步观察结果发现，年龄和季节是报告的分类和预测中最重要的预测变量。
本研究的结果可用于预测和控制绵羊和山羊的 BT 风险因素，与传统和常用的 LR 模型相比，ML 模型在准确性、TPR、FNR、FPR 和精度方面具有更好的诊断区分能力。我们的研究结果表明，在农场决策和预测中实施 ML 算法，主要是 RF，是分析横截面研究的一种很有前途的技术，可以提供充足的预测能力，并在识别和排名代表 BT 潜在风险因素的预测因子方面具有显著的竞争力。
