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

  • S. Nuanmeesri Faculty of Science and Technology, Suan Sunandha Rajabhat University, Thailand
  • W. Sriurai Faculty of Science, Ubon Ratchathani University, Thailand


This research aims to develop the analysis model for diseases in water buffalo towards the application of the feature selection technique along with the Multi-Layer Perceptron neural network. The data used for analysis was collected from books and documents related to diseases in water buffalo and the official website of the Department of Livestock Development. The data consists of the characteristics of six diseases in water buffalo, including Anthrax disease, Hemorrhagic Septicemia, Brucellosis, Foot and Mouth disease, Parasitic disease, 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. Afterward, the adjusted dataset was used to select the disease characteristics towards the application of two feature selection techniques, including Correlation-based Feature Selection and Information Gain. Subsequently, the selected features were then used for developing the analysis model for diseases in water buffalo towards the use of Multi-Layer Perceptron neural network. The evaluation results of the model’s effectiveness, given by the 10-fold cross-validation, showed that the analysis model for diseases in water buffalo developed by Correlation-based Feature Selection and Multi-Layer Perceptron neural network provided the highest level of effectiveness with the accuracy of 99.71%, the precision of 99.70%, and the recall of 99.72%. This implies that the analysis model is effectively applicable.

Keywords: water buffalo diseases, feature selection, multi-layer perceptron, neural network, synthetic minority over-sampling

Author Biography

W. Sriurai, Faculty of Science, Ubon Ratchathani University, Thailand




Download data is not yet available.


Thailand's livestock statistics. Bangkok, Thailand: Information and Communication Technology Center, Department of Livestock Development, 2015.

N. Hongboonmee and P. Sornrung, "Applying decision tree classification techniques for diagnose the disease in cow on mobile phone," Journal of Science and Technology, vol. 20, pp. 44-58, 2018.

P. Booranamanas, Water buffaloes and treatments, Bangkok, Thailand: Thaiwattanapanich, 1988.

M. G. Tsipouras, "Uterine EMG Signals Spectral Analysis for Pre-Term Birth Prediction," Engineering, Technology & Applied Science Research, vol. 8, no. 5, pp. 3310-3315, Oct. 2018.

P. Paranya, "Improving decision tree technique in imbalanced data sets using SMOTE for internet addiction disorder data," Information Technology Journal, vol. 12, no. 1, pp. 54-62, Jum. 2016.

A. H. Mark, "Correlation-based feature selection for machine learning," Ph.D. dissertation, The University of Waikato, Hamilton, New Zealand, 1999.

T. Puripat, "Ensemble algorithm for feature selection", M.S. thesis, Thammasat University, Bangkok, Thailand, 2016.

S. Boubaker, S. Kamel, and M. Kchaou, "Prediction of Daily Global Solar Radiation using Resilient-propagation Artificial Neural Network and Historical Data: A Case Study of Hail, Saudi Arabia," Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5228-5232, Feb. 2020.

A. Montaphan, "Comparison of feature selection methods to improve breast cancer prediction," Royal Thai Air Force Medical Gazette, vol. 65, no. 2, pp. 49-56, 2019.

M. B. Ayed, "Balanced Communication-Avoiding Support Vector Machine when Detecting Epilepsy based on EEG Signals," Engineering, Technology & Applied Science Research, vol. 10, no. 6, pp. 6462-6468, Dec. 2020.

S. Nuanmeesri, "Mobile application for the purpose of marketing, product distribution and location-based logistics for elderly farmers," Applied Computing and Informatics, 2019.

A. N. Saeed, "A Machine Learning based Approach for Segmenting Retinal Nerve Images using Artificial Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 5986-5991, Aug. 2020.

W. Kesornsit, V. Lorchirachoonkul, and J. Jitthavech, "Imbalanced data problem solving in classification of diabetes patients," Khon Kaen University Research Journal, vol. 18, no. 3, pp. 11-21, Jul. 2018.

A. Khemphila and V. Boonjing, "Heart Disease Classification Using Neural Network and Feature Selection," presented at the Proceedings - ICSEng 2011: International Conference on Systems Engineering, Las Vegas, NV, USA, Sep. 2011, vol. 64, pp. 406-409.

M. A. Hambali and M. D. Gbolagade, "Ovarian cancer classification using hybrid synthetic minority over-sampling technique and neural network," Journal of Advances in Computer Research, vol. 7, no. 4, pp. 109-124, Nov. 2016.

S. K. Hegde and R. Hedge, "Symmetry Based Feature Selection with Multi layer Perceptron for the prediction of Chronic Disease," International Journal of Recent Technology and Engineering, vol. 8, no. 2, pp. 3316-3322, Jul. 2019.


Abstract Views: 62
PDF Downloads: 29

Metrics Information
Bookmark and Share

Most read articles by the same author(s)