Comparison of Artificial Neural Networks, Random Forest Algorithm and Logistic Regression Analysis Performances in Classification of Effective Factors on Calf Diseases

Abstract

In this study, it is aimed to classify the factors affecting calf diseases with Artificial Neural Networks (YSA), Random Forest Algorithm (RO) and Logistic Regression Analysis (LR), to reveal the usability of these methods and to compare their performances. The research material consisted of the farm records of 54 calves kept in Erciyes Universi-ty, Agricultural Research and Application Center between 2018-2021. In the statistical analysis, the disease history of the calves was the dependent variable; calves gender, breed, birth season, maternal breed, and maternal lactation number were determined as independent variables. Classification performances were compared with sensitivity, speci-ficity, precision, accuracy, f-measure, Youden index, area under the ROC curve (AUC) and Cohen's kappa coefficient. According to the research results, the most successful classifiers in terms of sensitivity, selectivity, precision, accuracy, F-measure, Youden's index and Cohen's kappa; LR (0.828), ANN (0.947), ANN (0.964), ANN (0.833), ANN (0.857), ANN (0.719), ANN (0.663), respectively. In conclusion, it has been reached that be used classification methods cor-rectly classify the factors affecting calf diseases with a somewhat margin of error and YSA is more successful for all performance values except sensitivity. It is thought that these methods will help determine calf diseases with proactive approaches and prevent economic losses in livestock enterprises.