Vet World   Vol.18   September-2025  Article - 26 

Comparative application of machine learning approaches for body weight prediction in non-descript indigenous goats at different growth stages

Background and Aim: Accurate prediction of body weight (BW) in goats is vital for breeding, feeding, drug administration, and marketing decisions, particularly in resource-limited farming systems where weighing scales are often unavailable. Traditional regression models have been applied but are limited by multicollinearity and non-linearity in body measurement data. This study aimed to evaluate the predictive performance of two machine learning (ML) approaches – Classification and Regression Trees (CART) and Multivariate Adaptive Regression Splines (MARS) – for estimating BW in non-descript indigenous goats across birth, weaning, and yearling stages, compared with stepwise regression models.

Materials and Methods: A total of 100 goats were assessed at three growth stages: Birth (24 h), weaning (4 months), and yearling (12 months). Linear body measurements, body length (BL), sternum height, heart girth (HG), rump height, and withers height, were recorded alongside BW. Correlation analyses, stepwise regression, CART, and MARS models were developed. Model performance was evaluated using the coefficient of determination (R2), Pearson’s correlation coefficient (r), Akaike information criterion (AIC), and relative root mean square error (RMSE).

Results: BW showed strong positive correlations with HG and BL across all stages, while associations varied with other mor­phometric traits. Stepwise regression models exhibited lower predictive power, as indicated by reduced R² values and higher RMSE and AIC scores. In contrast, ML approaches demonstrated superior accuracy. CART consistently outperformed MARS, with R2 values of 0.87, 0.94, and 0.99 at birth, weaning, and yearling, respectively. CART also exhibited the highest r values (up to 0.99) and lowest RMSE across training and test datasets.

Conclusion: ML techniques, particularly CART, provide robust and reliable prediction of BW in non-descript indigenous goats, surpassing conventional regression methods. These approaches can guide practical herd management decisions, including optimized feed allocation, drug dosage, and breeding selection, especially in resource-limited settings. While the study underscores CART’s effectiveness, further validation with larger datasets and additional morphometric traits is rec­ommended to enhance generalizability.

Keywords: body weight prediction, classification and regression tree, growth stages, Indigenous goats, machine learning, multivariate adaptive regression splines.

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How to cite this article: Tyasi TL (2025) Comparative application of machine learning approaches for body weight prediction in non-descript indigenous goats at different growth stages, Veterinary World, 18(9):2878–2887.

Received: 09-10-2024   Accepted: 25-08-2025   Published online: 30-09-2025

Corresponding author: Thobela Louis Tyasi    E-mail: louis.tyasi@ul.ac.za

DOI: 10.14202/vetworld.2025.2878-2887

Copyright: Tyasi, et al. This article is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http:// creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.