Prediction of immunoglobulin G in lambs with artificial intelligence methods

Basit Öğe Kaydı
dc.authorid0000-0001-7958-7251
dc.authorid0000-0002-6660-2149
dc.authorid0000-0003-1261-4352
dc.authorid0000-0003-2674-1010
dc.authorid0000-0003-1183-6076
dc.contributor.authorCihan, Pınar
dc.contributor.authorGökçe, Erhan
dc.contributor.authorAtakişi, Onur
dc.contributor.authorKırmzıgül, Ali Haydar
dc.contributor.authorErdoğan, Hidayet Metin
dc.date.accessioned2021-05-03T07:13:18Z
dc.date.available2021-05-03T07:13:18Z
dc.date.issued2021
dc.departmentVeteriner Fakültesi
dc.description**Erdoğan, Hidayet Metin ( Aksaray, Yazar )
dc.description.abstractThe health, mortality and morbidity rates of neonatal ruminants depend on colostrum quality and the amount of Immunoglobulin G (IgG) absorbed. Computer-aided estimates are important as measuring IgG concentration with conventional methods is costly. In this study, artificial neural network (ANN), multivariate adaptive regression splines (MARS), support vector regression (SVR) and fuzzy neural network (FNN) models were used to predict the serum IgG concentration from gamma-glutamyl transferase (GGT) enzyme activity, total protein (TP) concentration and albumin (ALB). The correlation between parameters was examined. IgG positively correlated with GGT and TP and negatively correlated with ALB (R = 0.75, P<0.001; R = 0.67, P<0.001; R = -0.17, P<0.01, respectively). IgG, GGT, and TP cut-off values were determined for mortality, healthy, and morbidity in neonatal lambs by decision tree method. IgG <= 113 mg/dL (P<0.001), GGT 5191 mg/dL (P=0.001), and TP <= 45 g/L (P<0.001) were determined for mortality. IgG >575 mg/dL (P.0.02), GGT >191 mg/dL (P<0.001), and TP >55 g/L (P<0.001) were determined for healthy. It has been observed that the FNN is the most successful method for the prediction of IgG value with a correlation coefficient (R) of 0.98, root mean square error (RMSE) of 234.4, and mean absolute error (MAE) of 175.8.
dc.identifier.doi10.9775/kvfd.2020.24642
dc.identifier.endpage27en_US
dc.identifier.issue1en_US
dc.identifier.scopusqualityN/A
dc.identifier.startpage21en_US
dc.identifier.urihttps:/dx.doi.org/ 10.9775/kvfd.2020.24642
dc.identifier.urihttps://hdl.handle.net/20.500.12451/7920
dc.identifier.volume27en_US
dc.identifier.wosWOS:000608839200004
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherKafkas Üniversitesi
dc.relation.ispartofKafkas Üniversitesi Veteriner Fakültesi Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial Neural Network
dc.subjectDecision Tree
dc.subjectFuzzy Neural Network
dc.titlePrediction of immunoglobulin G in lambs with artificial intelligence methods
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
cihan-pinar-2021.pdf
Boyut:
1.46 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Makale Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
TR-Dizin İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu