The use of a rule-based module as a decision support system for dystocia detection in dairy cows

Abstract

The aim of the present study was to construct a rule-based module (RBM) for dystocia detection in dairy cattle and to verify its predictive performance. A total of 3041 calving records of Polish Holstein-Friesian Black-and-White heifers and cows were used. Three continuous and seven categorical predictors of dystocia were included in the three decision tree models, from which the rules for RBM were extracted. The system was equipped with a user-friendly text interface. The percentage of correctly detected easy, moderate and difficult calvings in heifers on the independent test set was 26.13%, 76.52% and 77.27%, respectively. The overall accuracy was 60.12%. The respective values for cows were: 59.18%, 69.01%, 0% and 62.03%. The predictive performance of the constructed system was satisfactory, except for the difficult category in cows.