Use of Multivariate Adaptive Regression Splines, Classification Tree and Roc Curve in Diagnosis of Subclinical Mastitis in Dairy Cattle

Abstract

Subclinical mastitis is one of the most significant diseases that cause economic losses in dairy cattle farming. This investigation was conducted on 112 head Holstein Friesian cows in order to reveal relationship between subclinical mastitis and electrical conductivity milk composition and milk quality. In the study, CMT (California Mastitis Test) and CSCC (Classified Somatic Cell Count) used in diagnosis of subclinical mastitis were used as a binary response variable i.e. healthy and unhealthy. Potential predictors included here were lactation number, days in milk (DIM), L, a, b, H, C, milk fat, milk protein, lactose, milk freezing point, SNF, density, solids, pH and electrical conductivity. CART, CHAID, Exhaustive CHAID, QUEST and multivariate adaptive regression splines (MARS) were used as data mining algorithms that help to make an accurate decision about detecting influential factors increasing risk of subclinical mastitis.In conclusion, better classification performances of CART and MARS data mining algorithms were determined compared with those of remaining algorithms in order to correctly discriminate healthy and unhealthy cows.