Comparison of data mining algorithms and regression method for prediction of live body weight using biometric traits of non-descript indigenous chickens.
- 1Department of Agricultural Economics and Animal Production, University of Limpopo, Private Bag X1106, Sovenga, Limpopo, 0727, South Africa.
- 2Department of Agricultural Economics and Animal Production, University of Limpopo, Private Bag X1106, Sovenga, Limpopo, 0727, South Africa. [email protected].
Abstract
The study aimed to investigate the relationship between live body weight and biometric traits, and to estimate live body weight from biometric traits of non-descript indigenous chickens. A total of 400 non-descript indigenous chickens (113 males and 287 females) were randomly selected from a multi-stage purposive sampling procedure following a cross-sectional study design. Live body weight (LBW), body length (BL), beak length (BKL), shank length (SL), shank circumference (SC), and comb height (CH) were measured. Data was analysed using Pearson's correlation, CHAID, CART, and stepwise linear regression. Correlation results revealed that LBW of males had a positive and high correlation (P < 0.01) with BL (r = 0.63), and LBW of females had a significant positive correlation (P < 0.01) with SC (r = 0.45). Regression results showed that for males, model combining BL, BKL, CH and SC (R² = 0.62, RMSE = 15.26, AIC = 2183.27, BIC = 2199.23) was best in estimation of LBW, while for females, model combining SC, BL, BKL and CH (R² = 0.36, RMSE = 12.31, AIC = 2011.20, BIC = 2027.17) was best in estimation of LBW. CHAID results revealed that SC was the best variable to estimate LBW, while CART model revealed that sex was the best variable for estimation of LBW. CHAID model (r = 0.79, R² = 0.63, RMSE = 11.73, AIC = 1969.45, BIC = 1973.44) outperformed stepwise regression and CART models. The study concludes that there was no model that had a good performance to predict live body weight of the chickens.
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BibTeX
@article{tsenane2026comparison,
title = {{Comparison of data mining algorithms and regression method for prediction of live body weight using biometric traits of non-descript indigenous chickens.}},
author = {Joas Albino Tsenane and Thabang Sako and Madumetja Cyril Mathapo and Thobela Louis Tyasi and Obert Tada},
journal = {Tropical animal health and production},
year = {2026},
month = oct,
doi = {10.1007/s11250-026-05391-1},
url = {https://europepmc.org/article/MED/42894042},
note = {CC BY},
}
RIS
TY - JOUR
TI - Comparison of data mining algorithms and regression method for prediction of live body weight using biometric traits of non-descript indigenous chickens.
AU - Joas Albino Tsenane
AU - Thabang Sako
AU - Madumetja Cyril Mathapo
AU - Thobela Louis Tyasi
AU - Obert Tada
PY - 2026
DA - 2026/10/18
JO - Tropical animal health and production
DO - 10.1007/s11250-026-05391-1
UR - https://europepmc.org/article/MED/42894042
AB - The study aimed to investigate the relationship between live body weight and biometric traits, and to estimate live body weight from biometric traits of non-descript indigenous chickens. A total of 400 non-descript indigenous chickens (113 males and 287 females) were randomly selected from a multi-stage purposive sampling procedure following a cross-sectional study design. Live body weight (LBW), body length (BL), beak length (BKL), shank length (SL), shank circumference (SC), and comb height (CH) were measured. Data was analysed using Pearson's correlation, CHAID, CART, and stepwise linear regression. Correlation results revealed that LBW of males had a positive and high correlation (P < 0.01) with BL (r = 0.63), and LBW of females had a significant positive correlation (P < 0.01) with SC (r = 0.45). Regression results showed that for males, model combining BL, BKL, CH and SC (R² = 0.62, RMSE = 15.26, AIC = 2183.27, BIC = 2199.23) was best in estimation of LBW, while for females, model combining SC, BL, BKL and CH (R² = 0.36, RMSE = 12.31, AIC = 2011.20, BIC = 2027.17) was best in estimation of LBW. CHAID results revealed that SC was the best variable to estimate LBW, while CART model revealed that sex was the best variable for estimation of LBW. CHAID model (r = 0.79, R² = 0.63, RMSE = 11.73, AIC = 1969.45, BIC = 1973.44) outperformed stepwise regression and CART models. The study concludes that there was no model that had a good performance to predict live body weight of the chickens.
SN - 0049-4747
SN - 1573-7438
ER -