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Use of data mining algorithms in prediction of eggshell thickness from egg quality traits of Potchefstroom Koekoek layers

Article scientifique 2025 Anglais

Résumé

Abstract Egg quality is affected by lot of factors. Study was conducted to compare performance of data mining algorithms; Classification and regression tree (CART), Chi-square automatic interaction detection (CHAID), Exhaustive chi-square automatic interaction detection (Ex-CHAID) and Multivariate adaptive regression spline (MARS) in prediction of Potchefstroom Koekoek’s eggshell thickness from egg quality traits. 350 eggs were collected at 31st to 39th week to examine the egg quality traits. MARS with R2(0.86) revealed yolk ratio, shell weight, egg shape index, yolk ratio, shell ratio, albumen weight and albumen ratio as explanatory variables predicting eggshell thickness. CART with R2 (0.37), yolk/albumen ratio was noted to be influential predictor of eggshell thickness. CHAID and Ex-CHAID (R2= 0.35) discovered egg weight as the best predictor of eggshell thickness. MARS with R2(0.86) revealed yolk ratio, shell weight, egg shape index, yolk ratio, shell ratio, albumen weight and albumen ratio as explanatory variables predicting eggshell thickness. MARS had high r (0.925), R2 (0.856) and lower RMSE (0.129) and AIC (-975.331) compared to CHAID, Ex-CHAID and CART leading MARS to be the best data mining algorithm when predicting the eggshell thickness using egg quality traits.

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Molabe, K., Tyasi, T., Mbazima, V. (2025). Use of data mining algorithms in prediction of eggshell thickness from egg quality traits of Potchefstroom Koekoek layers. https://doi.org/10.1038/s41598-025-86356-6

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Auteur(s)

Kagisho Madikadike Molabe Thobela Louis Tyasi Vusi Mbazima

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