Detection of difficult calvings in dairy cows using neural classifier

Abstract. In this study, the detection of dairy cows with difficult calvings using artificial neural networks (ANN) and classification functions (CF) is presented. The set of 15 classification variables was used. The dependent variable was the class of calving difficulty: difficult or easy. Perceptrons with one (MLP1) and two (MLP2) hidden layers as well as radial basis function (RBF) networks were analyzed. The prepared classifiers were characterized by good quality. The accuracy amounted to 75–92 %. Only the RBF network had somewhat worse quality. The level of correct detection by ANN was also high. The sensitivity on a test set was 67–80 % at specificity of 61- 81 %. In the case of CF, a considerable disproportion between sensitivity (6 %) and specificity (99 %) was found. The variables with the greatest contribution to the determination of calving difficulty class were calving season, CYP19-PvuII genotype, pregnancy length and, to a lesser degree, other variables. The performed analyses proved the usefulness of ANN for the detection of cows with difficult calvings, whereas the detection by CF was inaccurate.