As a new modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression and approximation. In this study, the SVR models had been introduced and developed to predict body and carcass related characteristics of two strains of broiler chicken. To evaluate the prediction ability of SVR models, we compared its performance with that of neural network (NN) models. Evaluation of the prediction accuracy of models wasbased on the R2, MS error and bias.The variables of interest as model output were body weight, empty body weight, carcass, breast, drumstick, thigh, and wing weight in two strains of Ross and Cobb based on intake dietary nutrients, includingME (kcal/ bird per week), CP, TSAA, and Lys all as g/bird per week. A dataset composed of 64 measurements taken from each strain were used for this analysis, where 44 data lines were used for model training whereas the remaining 20lines were used to test the created models.The results of this study revealed that it is possible to satisfactorily estimate the body weight and carcass parts of the broiler chickens via their dietary nutrient intake. Through statistical criteria used to evaluate the performance of the SVR and NN models,the overall results demonstrate that the discussed models can be effective for accurate prediction of the body and carcass related characteristics investigated here. However, the SVR method achieved better accuracy and generalization than the NN method. This indicates that the new data mining technique (SVR model) can be used as an alternative modeling tool for NN models. However, further re-evaluation of this algorithm in the future is suggested.

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