Background: Statistical analyses have played a fundamental role in the scientific determination of production traits and environmental factors influencing meat productivity. In recent years, machine learning methods have been increasingly explored due to their potential to enhance the accuracy and efficiency of live weight prediction in sheep. 
 
Methods: In this study, the predictive performance of various machine learning algorithms for estimating body weight in Norduz sheep was comparatively evaluated. multilayer perceptron neural networks (MLPNN) and least squares support vector machines (LS-SVM) were employed, with various network configurations and hyperparameter combinations tested. Biometric measurements-namely age, height at withers (HW), body length (BL), chest width behind paddles (CW), chest depth (CD), chest girth (CG) and thigh circumference (TC)-were utilized as input variables, while body weight (BW) served as the target variable. 
 
Result: The MLPNN model configured using the Bayesian Regularization algorithm and the TanSig activation function yielded the lowest error rates and the highest generalization capability. Within the LS-SVM model, the most accurate predictions were obtained using the radial basis function (RBF) kernel, with optimal hyperparameters set at &sigma; = 5 and &gamma; = 10. Among the biometric traits, Chest Girth was identified as the most influential variable for predicting live weight across both models. Furthermore, Age and Height at Withers were found to be critical determinants in the neural network model, whereas Chest Depth and Chest Width were more prominent in the LS-SVM model.

Prediction of Norduz Sheep Live Weight using Multilayer Perceptron Neural Networks and Least Square Support Vector Machines