PREDICTING BODY WEIGHT OF THREE CHICKEN GENOTYPES FROM LINEAR BODY MEASUREMENTS USING MARS AND CART DATA MINING ALGORITHMS

Abstract

The aim of the current study was to predict the body weight from linear body measurements of Astrolope, Boschveld and indigenous Sacco genotype using Classification and regression tree (CART) and Multivariate Adaptive Regression Spline (MARS) algorithm.A total of 389 body weight (BW) records, including five continuous predictors such as Neck length (NL), body circumference (BC), shank length (SL), body length (BL) and shank circumference (SC) were used.The best model was selected based on goodness of fit, such as, standard deviation ratio (SDR), root mean square error (RMSE), coefficient of variation (CV), adjusted coefficient of determination (ARsq), coefficient of determination (Rsq) and Pearson's correlation coefficients (PC).The Rsq (%) values ranged from 59 (MARS) to 69 (CART).The lowest SDR was recorded by CART (0.56) and the highest by MARS (0.70).The CART was selected to be the best algorithm with sex, genotype, SC, SL, BL, NL, and BC as influential predictor of BW.The heaviest body weight on females of genotype (Boschveld, Sacco) was recorded when BL was less than 43 cm and BL higher than 47 cm.The goodness of fit criteria suggest that CART model outperformed the MARS model on predicting the body weight of the three genotypes.The findings will assist farmers in the prediction of body wight and selection of heavier chickens.