Detection of correct pregnancy status in lactating dairy cattle using MARS data mining algorithm

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dc.authoridCANGA BOGA, DEMET/0000-0003-3319-7084
dc.authoridBOGA, MUSTAFA/0000-0001-8277-9262
dc.contributor.authorCanga, Demet
dc.contributor.authorBoga, Mustafa
dc.date.accessioned2024-11-07T13:31:53Z
dc.date.available2024-11-07T13:31:53Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn this study, it is aimed to determine pregnancy outcomes by using multivariate adaptive regression splines (MARS) algorithm for classification type problems. For this purpose, data obtained from a private dairy farm in the Konya region of Turkiye in 2020 were used to determine pregnancy outcomes in holstein dairy cattle. It has been determined how to perform statistical analyses on solving classification-type problems with the MARS algorithm and how to use R packages (caret and earth) by creating an R script file. After the analysis, the MARS estimation equation was created and in finding the probability of being pregnant: While lactation period, cow age, number of lactations, insemination number, and total lactation milk yield variables are important, it was seen that 7-day mean milk yield and last lactation milk yield were not significant. Using the train function of the caret package, the number of terms that produce the highest accuracy and the degree of interaction are determined. Goodness-of-fit tests of the optimum model were calculated. Within the scope of the evaluation of the generalization ability of the model, training and test sets were created, the classification success graph of the MARS algorithm, the model building phase were summarized, and the generalization ability of the established model was measured. When the pregnancy status is taken as a positive reference, the correct classification rate (sensitivity) of the animal with positive pregnancy status was found to be 0.9574:the correct classification rate (specificity) of pregnant animals was found to be 0.8370. The overall classification ratio of the training set (accuracy) was found to be 0.8777. The area under the ROC curve (AUC) was found to be 0.947, which indicates that the optimum specificity value is close to 1.
dc.identifier.doi10.55730/1300-0128.4257
dc.identifier.endpage819
dc.identifier.issn1300-0128
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85148689052
dc.identifier.scopusqualityQ3
dc.identifier.startpage809
dc.identifier.trdizinid1147565
dc.identifier.urihttps://doi.org/10.55730/1300-0128.4257
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1147565
dc.identifier.urihttps://hdl.handle.net/11480/15099
dc.identifier.volume46
dc.identifier.wosWOS:000899553100005
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.relation.ispartofTurkish Journal of Veterinary & Animal Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectLogistic regression
dc.subjectclassification
dc.subjectbinary data
dc.subjecttrain and test set
dc.subjectHolstein breed
dc.titleDetection of correct pregnancy status in lactating dairy cattle using MARS data mining algorithm
dc.typeArticle

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