Investigation of the Relationships between Coat Colour, Sex, and Morphological Characteristics in Donkeys Using Data Mining Algorithms

SIMPLE SUMMARY: The donkey (Equus asinus) is an odd-toed ungulate and the smallest species in the Equidae family. It is characteristically short-legged with extremely long ears. The wild ancestor of the donkey is equally Equus asinus, which is generally known as the “African wild ass” and is reportedly still extant. Donkeys are the only ungual animal domesticated exclusively in Africa. By nature, donkeys are very companionable, calm, enduring, intelligent, prudent, playful, and keen to learn, and they enjoy the company of humans. In Turkey, donkeys are used for pack transport and riding in order to lessen the physical load on humans. This study was conducted to assess the prediction performance of various algorithms using the morphological traits, body coat colour distribution, and body measurements of donkeys raised in Turkey. ABSTRACT: This study was carried out in order to determine the morphological characteristics, body coat colour distribution, and body dimensions of donkeys raised in Turkey, as well as to determine the relationships between these factors. For this reason, the predictive performance of various machine learning algorithms (i.e., CHAID, Random Forest, ALM, MARS, and Bagging MARS) were compared, utilising the biometric data of donkeys. In particular, mean measurements were taken from a total of 371 donkeys (252 male and 119 female) with descriptive statistical values as follows: height at withers, 100.7 cm; rump height, 103.1 cm; body length, 103.8 cm; chest circumference, 112.8 cm; chest depth, 45.7 cm; chest width, 29.1 cm; front shin circumference, 13.5 cm; head length, 55 cm; and ear length, 22 cm. The body colour distribution of the donkeys considered in this study was calculated as 39.35% grey, 19.95% white, 21.83% black, and 18.87% brown. Model fit statistics, including the coefficient of determination (R(2)), mean square error, root-mean-square error (RMSE), mean absolute percentage error (MAPE), and standard deviation ratio (SD ratio), were calculated to measure the predictive ability of the fitted models. The MARS algorithm was found to be the best model for defining the body length of donkeys, with the highest R(2) value (0.916) and the lowest RMSE, MAPE, and SD ratio values (2.173, 1.615, and 0.291, respectively). The experimental results indicate that the most suitable model is the MARS algorithm, which provides a good alternative to other data mining algorithms for predicting the body length of donkeys.