Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models
2020
Hend Radwan | Hadeel El Qaliouby | Eman Abo Elfadl
Objective: The objective of this study was to assess the veracities of most admired strategy dis¬criminant 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 alter¬native 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: The findings of the comparison indicated that ANN was more impressive in the expec¬tancy of milk production level than did an imitator statistical method based on DA. The accuracy of the ANN model was high for the winter season (79.5%), whereas it was 47.3% for DA. The cur¬rent findings were assured via the areas under receiver operating characteristic curves (AUROC) for DA and ANN. AUROC curves were smaller in the condition of the DA model across different calving seasons compared with the ANN model. The inaccuracies of variations were significant at a 5% significance level utilizing paired sample t-test. Conclusion: ANN model can be used efficiently to predict the level of production across the differ¬ent calving seasons compared to the DA model. [J Adv Vet Anim Res 2020; 7(3.000): 429-435]
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