Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models
Hend Radwan, Hadeel El Qaliouby, Eman A. Abo Elfadl
Abstract
OBJECTIVE: The objective of this study was to assess the veracities of most admired strategy discriminant analysis (DA), in comparison to the artificial neural network (ANN) for the anticipation and classification of milk production level in Holstein Friesian cattle using their performances. MATERIALS AND METHODS: A total of 3,460 performance records of imported and locally born Holstein Friesian cows were gathered during the period from 2000 to 2016 to compare two alternative techniques for predicting the level of production based on performance traits in dairy cattle with the use of statistical software (Statistical Package for the Social Sciences, version 20.0). RESULTS: -test. CONCLUSION: ANN model can be used efficiently to predict the level of production across the different calving seasons compared to the DA model.