Phenotypic Characterization of Hair and Honamli Goats Using Classification Tree Algorithms and Multivariate Adaptive Regression Spline (MARS)

Altay Y.

KAFKAS UNIVERSITESI VETERINER FAKULTESI DERGISI, vol.28, no.3, pp.401-410, 2022 (SCI-Expanded)

  • Publication Type: Article / Article
  • Volume: 28 Issue: 3
  • Publication Date: 2022
  • Doi Number: 10.9775/kvfd.2022.27163
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, EMBASE, Veterinary Science Database, Directory of Open Access Journals, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.401-410
  • Eskisehir Osmangazi University Affiliated: Yes


Some morphological and physiological data are needed to scientifically describe animals and distinguish breeds from one another. Except for those who are not experts in the field, it is difficult to distinguish goat breeds from each other. Using data mining algorithms, this study aimed to develop a new phenotypic characterization for Honamli and Hair goats via some body measurement characteristics. In the study, some body characteristics of the Hair goat (65 animals) and the Honamli goat (83 animals) were used as independent variables. The dependent variable of the data mining algorithms, on the other hand, was defined as the binary response variable of Honamli and Hair breeds. The success of the CHAID, Exhaustive CHAID, CART, QUEST, and MARS algorithms in breed discrimination was determined at 87.80%, 85.80%, 87.80%, 77.00%, and 88.51%, respectively, while the area under the ROC curve was detected 0.880, 0.853, 0.868, 0.784, and 0.942, respectively, and Cohen’s Kappa coefficient (κ) 0.755, 0.711, 0.749, 0.549 and 0.739, respectively. As a result, the phenotype characterization of Honamli and Hair goats, whose morphological distinctions could not be made exactly, in MARS and CHAID algorithms, achieved with high success compared to other methods. The present study showed that Honamli and Hair goats may be distinguished by suitable statistical algorithms based on morphological data, which can be integrated with goat breeding studies to detect the origin of breeding animals.