Use of Multivariate Adaptive Regression Splines, Classification Tree and Roc Curve in Diagnosis of Subclinical Mastitis in Dairy Cattle
نویسندگان
چکیده
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 order to reveal relationship between subclinical and electrical conductivity milk composition quality. In study, CMT (California Mastitis Test) CSCC (Classified Somatic Cell Count) used diagnosis were as a binary response variable i.e. healthy unhealthy. Potential predictors included here lactation number, days (DIM), L, a, b, H, C, fat, protein, lactose, freezing point, SNF, density, solids, pH conductivity. CART, CHAID, Exhaustive QUEST multivariate adaptive regression splines (MARS) data mining algorithms help make an accurate decision about detecting influential factors increasing risk mastitis.In conclusion, better classification performances CART MARS determined compared with those remaining correctly discriminate unhealthy cows.
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ژورنال
عنوان ژورنال: Journal of the Hellenic Veterinary Medical Society
سال: 2022
ISSN: ['1792-2720', '2585-3724']
DOI: https://doi.org/10.12681/jhvms.25864