%0 Journal Article
%T Parkinson¡¯s Disease Diagnosis: Detecting the Effect of Attributes Selection and Discretization of Parkinson¡¯s Disease Dataset on the Performance of Classifier Algorithms
%A Gamal Saad Mohamed
%J Open Access Library Journal
%V 3
%N 11
%P 1-11
%@ 2333-9721
%D 2016
%I Open Access Library
%R 10.4236/oalib.1103139
%X
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.
%K PD
%K SVM
%K MLP
%K Decision Tree
%K Naive Bayes
%K Classifier
%U http://www.oalib.com/paper/5275815