Evaluating the Performance of Three Classification Methods in Diagnosis of Parkinson’s Disease
Accurate diagnosis of the Parkinson’s disease is a challenging task that involves many physical, psychological and neurological examinations. The examinations include investigating a number of signs and symptoms, reviewing the medical history and checking the nervous system conditions of a patient. Recently, researchers use voice disorders to diagnose Parkinson’s disease patients. They extract features of a recorded human voice and apply classification methods to diagnosis this disease. In this paper, we apply a Decision Tree, Naïve Bayes and Neural Network classification methods for the diagnosis of Parkinson’s disease. The aim of this paper is to resolve the problem by evaluating the performance of the three methods. The objectives of the paper are to (i) implement three classification methods independently on a Parkinson’s dataset, and (ii) determine the best method among the three. The classification results show that the Decision Tree produces the highest accuracy rate of 91.63%, followed by the Neural Network, 91.01% and the Naïve Bayes produces the lowest accuracy rate of 89.46%. The results recommend using the Decision Tree or the Neural Network over the Naïve Bayes for datasets with similar properties.
KeywordsClassification Decision tree Naïve bayes Neural network
This project is sponsored by Universiti Tun Hussein Onn Malaysia, ORICC, under Vot D004.
- 3.Sutherland, M., Dean, P.: What is Parkinson’s Disease? Neuro Challenges, Foundation for Parkinson’s. http://www.parkinsonsneurochallenge.org (2017). Accessed 08 June 2017
- 4.Asuncion, A., Newman, D.: UCI machine learning repository. https://archive.ics.uci.edu/ml/datasets/parkinsons (2007)
- 7.Gnanapriya, S., Suganya, R., Devi, G.S., Kumar, M.S.: Data mining concepts and techniques. Data Min. Knowl. Eng. 2(9), 256–263 (2010)Google Scholar
- 8.Tatu, A., Albuquerque, G., Eisemann, M., Schneidewind, J., Theisel, H., Magnor, M., Keim, D.: Combining automated analysis and visualization techniques for effective exploration of high-dimensional data. In: 2009 IEEE Symposium on Visual Analytics Science and Technology VAST 2009, pp. 59–66. IEEE (2009)Google Scholar
- 10.Exarchos, T.P., Tzallas, A.T., Baga, D., Chaloglou, D., Fotiadis, D.I., Tsouli, S., Konitsiotis, S.: Using partial decision trees to predict Parkinson’s symptoms: a new approach for diagnosis and therapy in patients suffering from Parkinson’s disease. Comput. Biol. Med. 42(2), 195–204 (2012)Google Scholar
- 11.Can, M.: Neural networks to diagnose the Parkinson’s disease. SouthEast Eur. J. Soft Comput. 2(1) (2013)Google Scholar
- 12.Mohammed, M.A., Ghani, M.K.A., Hamed, R.I., Mostafa, S.A., Ibrahim, D.A., Jameel, H.K., Alallah, A.H.: Solving vehicle routing problem by using improved K-nearest neighbor algorithm for best solution. J. Comput. Sci. (2017)Google Scholar
- 13.Khaleefah, S.H., Nasrudin, M.F., Mostafa, S.A.: Fingerprinting of deformed paper images acquired by scanners. In: 2015 IEEE Student Conference on Research and Development (SCOReD), pp. 393–397. IEEE, Dec 2015Google Scholar
- 14.Mohammed, M.A., Gani, M.K.A., Hamed, R.I., Mostafa, S.A., Ahmad, M.S., Ibrahim, D.A.: Solving vehicle routing problem by using improved genetic algorithm for optimal solution. J. Comput. Sci. (2017)Google Scholar
- 16.Kumar, S.A., Vijayalakshmi, M.: Efficiency of decision trees in predicting student’s academic performance. In: The First International Conference on Computer Science, Engineering and Applications, CS and IT, vol. 2, pp. 335–343Google Scholar
- 17.Rennie, J.D., Shih, L., Teevan, J., Karger, D.R.: Tackling the poor assumptions of naive bayes text classifiers. In: Proceedings of the International conference on Machine Learning ICML, Vol. 3, pp. 616–623 (2003) Google Scholar
- 18.Bahramirad, S., Mustapha, A., Eshraghi, M.: Classification of liver disease diagnosis: a comparative study. In: 2013 Second International Conference on Informatics and Applications (ICIA), pp. 42–46. IEEE, Sept 2013Google Scholar