EXPLORING DATA MINING ALGORITHMS FOR PREDICTING DUCK EGG WEIGHT BASED ON EGG QUALITY CHARACTERISTICS

L. Dahloum^1,*, Q. Benameur¹, and A. Yakubu²

¹Laboratory of Agrobiotechnology, Genetic Resources, and Modelling, Abdelhamid Ibn Badis University, PO.Box 188, Mostaganem 27000, Algeria

²Department of Animal Science, Faculty of Agriculture, Nasarawa State University, Keffi, Shabu-Lafia Campus, P.M.B. 135, Lafia 950101, Nigeria

^*Corresponding author’s email: Lahouari.dahloum@univ-mosta.dz

ABSTRACT

The present investigation aimed to compare the performance of two machine learning algorithms, Artificial Neural Network (ANN), and Classification and Regression Tree (CART), alongside the Automatic Linear Modelling (ALM), and the traditional Multivariate Linear Regression model (MLR) to predict the egg weight (EWT) of Mallard duck from some egg traits including egg length (EL), egg width (EWd), egg shape index (ESI), eggshell weight (ESW), albumen weight (AW), albumen height (AH), yolk weight (YW), yolk height (YH), yolk diameter (YD), and Haugh unit (HU). The Pearson correlation between observed and predicted values (r), coefficient of determination (R²), adjusted coefficient of determination (R²_adj), Root Mean Squared Error (RMSE), and Relative Approximation Error (RAE) were used to estimate model performance. EWT had a strong correlation with egg dimensions (EL and EWd, r=0.752 and 0.790, respectively), AW (r= 0.815), and YW (r= 0.784). The R²_adj values were 0.981, 0.970, 0.964 and 0.897, for ANN, ALM, MLR, and CART models, respectively. The lowest RMSE was found for ANN (0.753), while the highest RMSE was observed for CART (1.778). Overall, the ensemble models proposed in this study yielded similar results, with the ANN algorithm showing a marginally superior predictive performance compared to ALM, CART, and MLR models. This finding suggests that ANN could be considered the most suitable for the prediction of egg weight in Mallard duck.