Thai Water Buffalo Disease Analysis with the Application of Feature Selection Technique and Multi-Layer Perceptron Neural Network

Abstract

This research aims to develop an analysis model for diseases of the water buffalo with the application of the feature selection technique along with the Multi-Layer Perceptron Neural Network (MLP-NN). The data used for analysis were collected from books and documents related to water buffalo diseases and the official website of the Department of Livestock Development. The data consist of the characteristics of 6 water buffalo diseases, including anthrax, hemorrhagic septicemia, brucellosis, foot and mouth diseases, parasitic diseases, and mastitis. Since the amount of the collected data was limited, the synthetic minority over-sampling technique was also employed to adjust the imbalance dataset. The adjusted dataset was used to select the disease characteristics towards the application of two feature selection techniques, correlation-based feature selection and information gain. Subsequently, the selected features were then used for developing the analysis model for water buffalo diseases towards the use of the MLP-NN. The evaluation results given by 10-fold cross-validation, showed that the analysis model for water buffalo diseases developed by correlation-based feature selection and MLP-NN provided the highest level of effectiveness with an accuracy of 99.71%, precision of 99.70%, and recall of 99.72%, implying that the analysis model is effectively applicable.