Body weight prediction using different data mining algorithms in Thalli sheep: A comparative study

Abstract

Background and Aim: The Thalli sheep are the main breed of sheep in Pakistan, and an effective method to predict their body weight (BW) using linear body measurements has not yet been determined. Therefore, this study aims to establish an algorithm with the best predictive capability, among the Chi-square automatic interaction detector (CHAID), exhaustive CHAID, artificial neural network, and classification and regression tree (CART) algorithms, in live BW prediction using selected body measurements in female Pakistani Thalli sheep. Materials and Methods: A total of 152 BW records, including nine continuous predictors (wither height, body length [BL], head length, rump length, tail length, head width, rump width, heart girth [HG], and barrel depth), were utilized. The coefficient of determination (R2), standard deviation ratio, root-mean-square error (RMSE), etc., were calculated for each algorithm. Results: The R2 (%) values ranged from 49.28 (CART) to 64.48 (CHAID). The lowest RMSE was found for CHAID (2.61), and the highest one for CART (3.12). The most significant predictors were the HG of live BW for all algorithms. The heaviest average BW (41.12 kg) was observed in the subgroup of those having a BL of >73.91 cm (Adjusted p=0.045). Conclusion: Among the algorithms, CHAID provided the most appropriate predictive capability in the prediction of live BW for female Thalli sheep. In general, the applied algorithms accurately predicted the BW of Thalli sheep, which can be very helpful in deciding on the standards, available drug doses, and required feed amount for animals.

Predicting body weight of Kalahari Red goats from linear body measurements using data mining algorithms

“…The cross-validation was kept at 10, as recommended by Celik and Yilmaz [19]. The following goodness of fit was used to execute the predictive performance of CART, CHAID, and Ex-CHAID [10]. ( )…”

“…Nevertheless, Celik and Yilmaz [19] compared CART, CHAID, and Ex-CHAID and established that CHAID was the ideal algorithm with an R 2 of 71.58% and SD ratio of 0.53 for estimating BW in dogs. Abbas et al [10] compared CART, CHAID, and Ex-CHAID for predicting BW for Thalli sheep and indicated that CHAID was the ideal algorithm with the predicted 64.48% R 2 and 0.59 SD ratio. The CART demonstrated that BL, HG, and wither height play critical roles in BW, inferring it can be chosen to Our records disagree with the findings of Mathapo and Tyasi [11] on Boar goats, and the investigation of Eyduran et al [13] on indigenous Beetal Goat of Pakistan revealed that sex played a crucial role in BW.…”

“…Therefore, statistical algorithms, such as classification and regression tree (CART), Chi-square automatic interaction detection (CHAID), and exhaustive CHAID (Ex-CHAID), were suggested. These statistical algorithms are a tree-based model which assesses characteristics (independent traits) and other factors, such as gender, contributing an essential role to the dependent variable [10]. The decision tree is extensively applied to estimate BW from categorical and numerical-dependent variables in Boer goats [11], Balochi sheep [12], Beetal goats of Pakistan [13], and Turkish Tazi dogs [14] to help farmers in remote areas who do not have weighing scales.…”

Evaluating the prediction performances of artificial neural network, nearest neighbor, and CART algorithms for body weight in Sujiang pigs using morphological measurements

“…For instance, K nearest neighbors, decision trees, and random forests were used in Holsteins to predict live body weight (Coşkun et al, 2022). CHAID, exhaustive CHAID, CART, and artificial neural networks were used as predictor algorithms for live body weight in Pakistani Thalli sheep (Abbas et al, 2021). The authors used morphometric measurements in the analysis.…”

Prediction of Body Weight from Linear Body Measurements in Kashmiri (Kashir) Goat

“…The most important reason among for not recording body weight being unavailability of weighing scales. Further, body weights are supplemented with linear body measurements which describe an animal or breed completely than the conventional methods of weighing or grading (Abbas et al, 2021). The linear body measurements of the animals are necessary for establishing breed standards, in judging the quantitative characteristics of meat and helpful in developing suitable selection criteria (Sharaby 1 Sheep Brereding Farm, Balnoi-185 211, Jammu and Kashmir, India.…”