title: Application of the support vector machine to predict subclinical mastitis in dairy cattle.
abstract: This study presented a potentially useful alternative approach to ascertain the presence of subclinical and clinical mastitis in dairy cows using support vector machine (SVM) techniques. The proposed method detected mastitis in a cross-sectional representative sample of Holstein dairy cattle milked using an automatic milking system. The study used such suspected indicators of mastitis as lactation rank, milk yield, electrical conductivity, average milking duration, and control season as input data. The output variable was somatic cell counts obtained from milk samples collected monthly throughout the 15 months of the control period. Cattle were judged to be healthy or infected based on those somatic cell counts. This study undertook a detailed scrutiny of the SVM methodology, constructing and examining a model which showed 89% sensitivity, 92% specificity, and 50% error in mastitis detection.
authors.affiliation: Department of Animal Science, Faculty of Agriculture, Siirt University, 56100 Siirt, Turkey.
authors.forename: Nazira
authors.initials: N
authors.lastname: Mammadova
authors.affiliation: Department of Animal Science, Faculty of Agriculture, Selçuk University, 42075 Konya, Turkey.
authors.forename: Ismail
authors.identifier: 0000-0001-9358-7522
authors.initials: I
authors.lastname: Keskin
mesh_terms: D000818:Animals; D001331:Automation; D002417:Cattle; D002452:Cell Count / Q000662:veterinary; D003612:Dairying / Q000379:methods; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data / Q000662:veterinary*; D003951:Diagnostic Errors / Q000662:veterinary; D004553:Electric Conductivity; D005260:Female; D007774:Lactation; D016015:Logistic Models; D008414:Mastitis, Bovine / Q000175:diagnosis*; D008892:Milk / Q000737:chemistry / Q000166:cytology; D060388:Support Vector Machine; D014421:Turkey