Application of the Support Vector Machine to Predict Subclinical Mastitis in Dairy Cattle
Joint Authors
Mammadova, Nazira
Keskin, İsmail
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-25
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Mammadova, Nazira& Keskin, İsmail. 2013. Application of the Support Vector Machine to Predict Subclinical Mastitis in Dairy Cattle. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1033097
Modern Language Association (MLA)
Mammadova, Nazira& Keskin, İsmail. Application of the Support Vector Machine to Predict Subclinical Mastitis in Dairy Cattle. The Scientific World Journal No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1033097
American Medical Association (AMA)
Mammadova, Nazira& Keskin, İsmail. Application of the Support Vector Machine to Predict Subclinical Mastitis in Dairy Cattle. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1033097
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1033097