The aim of this study was to predict carcass tissue composition of goat kids using the decision tree with CHAID algorithm (DT) and artificial neural network (ANN) method in comparison with classical step-wise regression (SWR) analyse. Data were obtained from 57 goat kids of Gokceada breed. Predictor variables were pre-slaughter weight, several carcass measurements and indices, weights of different carcass joints and dressing percentage. R² values ranging from 0.212 to 0.371 indicating low to moderate accuracy were obtained for predicting muscle proportion. DT and ANN yielded similar R² values for predicting bone proportion. DT was the best prediction method for estimating proportions of subcutaneous fat (R² = 0.828) and intermuscular fat (R² = 0.789). According to DT analyses, cold carcass weight was the most important factor influencing bone proportion, while kidney knob and channel fat weight was the predominant factor influencing subcutaneous, intermuscular and total fat proportions. Consequently, the use of DT method can be considered to predict carcass fat proportions.

这项研究的目的是与传统的逐步回归（SWR）分析相比，使用CHAID算法（DT）和人工神经网络（ANN）方法通过决策树预测山羊孩子的cas体组织组成。数据来自Gokceada品种的57个山羊羔。预测变量包括屠宰前体重，几次car体测量和指标，不同car体接缝的重量和修整百分比。获得的R ²值介于0.212到0.371之间，表示准确度从低到中等，可用于预测肌肉比例。DT和ANN得出的R ²值相似，可用于预测骨骼比例。DT是估计皮下脂肪（R ²  = 0.828）和肌间脂肪（R ²）比例的最佳预测方法 = 0.789）。根据DT分析，cold体重量过轻是影响骨骼比例的最重要因素，而肾钮和通道脂肪的重量则是影响皮下，肌肉间和总脂肪比例的主要因素。因此，可以考虑使用DT方法来预测car体脂肪比例。