Data mining of some factors affecting dystocia in Iranian dairy cows

Data mining explores hidden and invisible relationships and patterns in the vast amount of data without which these relationships may never be revealed. Determining the factors affecting dystocia in dairy cattle can help to find the weaknesses of the management of the dairy cattle industry and the resulting problems. Therefore, it is necessary to identify and manage the most important of the various genetic and non-genetic factors that affecting dystocia. The feature selection algorithm is one of the data mining methods that can be useful in this field. This study aimed to determine the most important factors affecting the dystocia of Holstein dairy cows using the feature selection algorithm. The total records were 413205, along with 14 features related to reproductive and production records. Data analyzed with four important methods of feature selection algorithm (Best-First, Greedy-Stepwise Genetic-search, and Ranker) and nine different models (CFS-Subset-Eval, Gain-Ratio-Attribute-Eval, etc.) to determine the most important factors. The results showed that the genetic-search method using Naive Bayes Tree classification of the feature selection was the most appropriate method for selecting the factors affecting dystocia with the lowest error rate (AMS = 0.011, ARAE = 0.001). The most important factors affecting dystocia were milk production, dam age at parturition, parity, gestational length, corrected milk based on 4% fat, calf sex, calf weight, the season of calving, type of calving, fat-to-protein percentage ratio, and open days. It is expected that by considering these factors, dystocia can be better managed in dairy cattle enterprises.