Prediction of Body Weight of Turkish Tazi Dogs using Data Mining Techniques: Classification and Regression Tree (CART) and Multivariate Adaptive Regression Splines (MARS)

Abstract

Body weight of dogs is crucial trait for breeding, racing and housekeeping. However, variables and factors that correctly estimate this trait are lacking. Here, we applied classification and regression tree (CART) and multivariate adaptive regression splines (MARS) approaches to estimate the most important variables in predicting the body weight of Turkish Tazi dogs. Using various body measurements, the CART algorithm proposed that withers height (WH), abdominal width (AW), rump height (RH) and chest depth (CD) can significant effect the body weight. Quantitatively, it was identified that values of WH > 62.500 cm and RH > 67.500 cm can positively correlated with the highest body weights. On the other hands, MARS model's finding showed that the dogs which had the values of WH > 51 cm can be expected to have the highest body weights. The calculated model evaluation criteria of CART algorithm was R2=0.6889, Adj. R2=0.6810, r=0.830, SD ratio=0.5549, RMSE=1.1802, RRMSE=6.3838 and p=3.4884, respectively, whereas the calculated model evaluation criteria of MARS method were R2=0.9193, Adj. R2=0.8983, r=0.9588, SD ratio=0.2840, RMSE=0.6041, RRMSE=3.2635 and rho=1.6661. Taken together, the MARS algorithm appeared to be efficient compared to CART algorithm since the MARS algorithm's goodness of-fit criteria yielded better results. Using MARS algorithm, the body weight of animals (dogs) can be predicted and exploited in different performances.