Zaborski Daniel, Proskura Witold S, Grzesiak Wilhelm
Department of Ruminants Science, West Pomeranian University of Technology, Szczecin 71-270, Poland.
Asian-Australas J Anim Sci. 2018 Nov;31(11):1700-1713. doi: 10.5713/ajas.17.0780. Epub 2018 Apr 12.
The aim of this study was to verify the usefulness of artificial neural networks (ANN), multivariate adaptive regression splines (MARS), naïve Bayes classifier (NBC), general discriminant analysis (GDA), and logistic regression (LR) for dystocia detection in Polish Holstein-Friesian Black-and-White heifers and cows and to indicate the most influential predictors of calving difficulty.
A total of 1,342 and 1,699 calving records including six categorical and four continuous predictors were used. Calving category (difficult vs easy or difficult, moderate and easy) was the dependent variable.
The maximum sensitivity, specificity and accuracy achieved for heifers on the independent test set were 0.855 (for ANN), 0.969 (for NBC), and 0.813 (for GDA), respectively, whereas the values for cows were 0.600 (for ANN), 1.000 and 0.965 (for NBC, GDA, and LR), respectively. With the three categories of calving difficulty, the maximum overall accuracy for heifers and cows was 0.589 (for MARS) and 0.649 (for ANN), respectively. The most influential predictors for heifers were an average calving difficulty score for the dam's sire, calving age and the mean yield of the farm, where the heifer was kept, whereas for cows, these additionally included: calf sex, the difficulty of the preceding calving, and the mean daily milk yield for the preceding lactation.
The potential application of the investigated models in dairy cattle farming requires, however, their further improvement in order to reduce the rate of dystocia misdiagnosis and to increase detection reliability.
本研究旨在验证人工神经网络（ANN）、多元自适应回归样条（MARS）、朴素贝叶斯分类器（NBC）、广义判别分析（GDA）和逻辑回归（LR）在波兰荷斯坦-弗里生黑白花小母牛和母牛难产检测中的有效性，并指出影响产犊困难的最具影响力的预测因素。
共使用了1342条和1699条产犊记录，包括6个分类预测因素和4个连续预测因素。产犊类别（难产与顺产或难产、中产和顺产）为因变量。
在独立测试集上，小母牛的最大灵敏度、特异性和准确率分别为0.855（ANN）、0.969（NBC）和0.813（GDA），而母牛的值分别为0.600（ANN）、1.000和0.965（NBC、GDA和LR）。对于难产的三个类别，小母牛和母牛的最大总体准确率分别为0.589（MARS）和0.649（ANN）。对小母牛最具影响力的预测因素是其母系公牛的平均产犊困难评分、产犊年龄以及小母牛所在农场的平均产量，而对于母牛，这些因素还包括：犊牛性别、前次产犊的难度以及前一胎次的平均日产奶量。
然而，所研究模型在奶牛养殖中的潜在应用需要进一步改进，以降低难产误诊率并提高检测可靠性。
