ArticleTropical animal health and production2022
Prediction of the live weight at breeding age from morphological measurements taken at weaning in indigenous Honamli kids using data mining algorithms.
Article in Tropical animal health and production, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 10 citations in OpenAlex.
- Comparative application of machine learning approaches for body weight prediction in non-descript indigenous goats at different growth stages.Veterinary world · 2025Article
- Development and validation of an insulin resistance prediction model in children and adolescents using machine learning algorithms.Translational pediatrics · 2025Article
- Estimating body weight in Sujiang pigs using artificial neural network, nearest neighbor, and CART algorithms: a comparative study using morphological measurements.Tropical animal health and production · 2025Article
- Machine learning-based early prediction of growth and morphological traits at yearling age in pure and hybrid goat offspring.Tropical animal health and production · 2024Article
- Using the artificial bee colony technique to optimize machine learning algorithms in estimating the mature weight of camels.Tropical animal health and production · 2023Article
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Abstract
The purpose of this study was to predict live weight at breeding age (LW) based on weaning morphological traits, which birth weight (BW), weaning weight (WW), withers height (WH), back height (BH), rump height (RH), chest depth (CD), body length (BL), tail length (TL), chest girth (CG), leg girth (LG), front shark circumference (FSC), head length (HL), head width (HW), nose length (NL), ear length (EL), and ear width (EW). For this purpose, measurements were taken from 84 Honamli kids born in 2018. The study also included sex, birth type (BT), and ear type as the nominal predictors. For this purpose, two MARS (Multivariate Adaptive Regression Splines), which are interaction (MARS2) and non-interaction (MARS1), and based-tree algorithms, such as CART (Classification and Regression Tree), CHAID (Chi-Square Automatic Interaction Detector), and Exhaustive CHAID, were used by cross-validation 5 and compared with each other considering the predictive performance by taking into account nine predictive performances criteria. LW has a significantly positive and high linear relationship with WH (0.770), BH (0.770), RH (0.750), BL (0.750), and CG (0.770), respectively (p < 0.01). According to these criteria, second-order interaction MARS2 model had the best performance among all data mining algorithms. Also, the CHAID algorithm was the best predictor of LW among regression tree-based algorithms. The CHAID algorithm predicted that the Honamli goat with 14.426 < WW < 15.575 kg and HW > 16.464 cm had the heaviest LW at 56.268 kg. The MARS2 model showed that the heaviest LW could be produced by WW > 16.10 kg, HW > 17 cm, Sex-Male × BL > 60 cm, WW × BL < 50 cm, BT-twin × WW < 15.60 kg, BL > 50 cm × CG > 62.4 cm and male goats. Also, CHAID and MARS2 algorithms explain 92.00% and 94.50% of the variation in LW, respectively. According to the results, it can be concluded that the CHAID and MARS algorithms used in the prediction of LW at breeding age could give an idea to reveal the breed standards examined for breeding purposes. While determining that there are important statistical methods in defining body characteristics at weaning in a complex way, the body characteristics determined by these models can be used as indirect selection criteria.
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