Comparison between data mining methods to assess calving difficulty in cattle

• Main Authors: Daniel Zaborski*, Witold S Proskura, Wilhelm Grzesiak
• Format: Article
• Language: English
• Published: Universidad de Antioquia
• Series: Revista Colombiana de Ciencias Pecuarias
• Subjects: classification; dairy heifers; decision support systems; dystocia; electronic learning
• Online Access: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-06902017000300196&lng=en&tlng=en

Abstract Background: Dystocia in cattle results in adverse consequences (increased calf morbidity and mortality, decreased fertility, and milk production, lower cow survival and reduced welfare) leading to considerable economic losses. Objective: To classify calvings in dairy cattle according to their difficulty using selected data mining methods (classification and regression trees (CART), chi-square automatic interaction detection trees (CHAID) and quick, unbiased, efficient, statistical trees (QUEST)), and to identify the most significant factors affecting calving difficulty. The results of data mining methods were compared with those of a more traditional generalized linear model (GLM). Methods: A total of 1,342 calving records of Polish Holstein- Friesian black-and-white heifers from four farms were used. Calving difficulty was divided into three categories (easy, moderate and difficult). Results: The percentages of calvings correctly classified by CART, CHAID, QUEST, and GLM were as follows: 35.14, 18.92, 19.82, and 43.24% (easy), 68.70, 73.91, 81.74, and 41.74% (moderate), and 77.27, 85.45, 73.64, and 81.82% (difficult), respectively. The most important factors affecting calving difficulty were bull’s rank (based on the mean calving difficulty score of its daughters), calving age, farm category (based on its mean milk yield) and calving season. Conclusion: All classification models were satisfactory and could predict the class of calving difficulty.