Parkinson’s Disease Diagnosis: Detecting the Effect of Attributes Selection and Discretization of Parkinson’s Disease Dataset on the Performance of Classifier Algorithms
Precise detection of PD is
important in its early stages. Precise result can be achieved through data
mining, classification techniques such as Naive Bayes, support vector machine
(SVM), multilayer perceptron neural network (MLP) and decision tree. In this
paper, four types of classifiers based on Naive Bayes, SVM, MLP neural network,
and decision tree (j48) are used to classify the PD dataset and the performances of these classifiers are examined when they are
implemented upon the actual PD dataset, discretized PD dataset, and selected
set of attributes from PD dataset. The dataset used in this study comprises a
range of voice signals from 31 people: 23 with PD and 8 healthy people. The
result shows that Naive Bayes and decision tree (j48) yield better accuracy
when performed upon the discretized PD dataset with cross-validation test mode
without applying any attributes selection algorithms. SVM gives high accuracy
of 70% for training and 30% for the test when implemented on a discretized PD
dataset and a splitting dataset. The MLP neural network gives the highest
accuracy when used to classify actual PD dataset without discretization, attribute
selection, or changing test mode.
Cite this paper
Mohamed, G. S. (2016). Parkinson’s Disease Diagnosis: Detecting the Effect of Attributes Selection and Discretization of Parkinson’s Disease Dataset on the Performance of Classifier Algorithms. Open Access Library Journal, 3, e3139. doi: http://dx.doi.org/10.4236/oalib.1103139.
Shen-Yang, L., Puvanarajah,
S.D. and
Ibrahim, N.M. (2011) Parkinson’s
Disease: Information for People Living with Parkinson’s. Novartis
Corporation (Malaysia) Sdn. Bhd. and Orient Europharma (M) Sdn. Bhd.
Islam, M.S., Parvez, I., Deng, H. and
Goswami,
P. (2014) Performance
Comparison of Heterogeneous Classifiers for Detection of Parkinson’s Disease
Using Voice Disorder (Dysphonia). 3rd International
Conference on Informatics, Electronics & Vision.
Sharma, A. and
Giri,
R.N. (2014) Automatic
Recognition of Parkinson Disease via Artificial Neural Network and Support
Vicyor Machine. IJITEE, 4, 35-41.
Hadjahamadi, A.H. and
Askari,
T.J. (2012) A Detection
Support System for Parkinson’s Disease Diagnosis Using Classification and
Regression Tree. Journal of Mathematics and Computer Science, 4,
257-263.
Olanrewaju, R.F., Sahari,
N.S., Musa, A.A. and
Hakiem, N. (2014) Application
of Neural Networks in Early Detection and Diagnosis of Parkinson’s
Disease. International Conference on Cyber and IT Service
Management.
Muhlenbach, F. and Rakotomalala, R. (2005)
Discretization of Continuous Attributes. In: Wang, J., Ed., Encyclopedia of Data Warehousing and Mining,
Idea Group Reference, 397-402.
Fayyad, U.M. and Irony, K.B. (1993)
Multi-Interval Discretization of Continuous Valued Attributes for
Classification Learning. 13th
International Joint Conference on Artificial Intelligence,
1022-1027.
Cristianini,
N. and Shawe-Taylor, J. (2000) An
Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge
University Press, Cambridge. http://dx.doi.org/10.1017/CBO9780511801389
Zhao, Y.H.
and Zhang, Y.X.
(2008)
Comparison of Decision Tree Methods for Finding Active Objects. Advances in Space
Research, 41, 1955-1959. http://dx.doi.org/10.1016/j.asr.2007.07.020