Using data mining
technique, we have gained higher prediction accuracy to detect PD compared with
the existing methods. We have also successfully compared two different workbenches
with same classifiers to identify the best result produced by them. It has been
found that ensemble method gives 100% accuracy using IBM SPSS Modeller 18
workbench. This paper also shows that the ensemble method shows higher accuracy
than individual classifiers in the case of Parkinson dataset. Not all
classifiers show better accuracy for all datasets. Using the ensemble method,
all single classifiers are used with
eliminating their limitations. It is quite difficult to test each classifier
available in workbench to see their accuracy on a particular dataset. This
problem has been solved successfully with the help of auto classifier node
available in SPSS Modeller 18 workbench. We have used 10-fold cross-validation
for training and testing in both workbenches to compare them with the same used
technique. The difference between their accuracy indicates that the
implementation detail is different in each workbench. Classification model j48
in Weka is similar to C5.0 in IBM SPSS Modeller 18 but C5.0 is an updated
version than j48 which is also known as C4.5. Weka is an open source workbench
whereas IBM SPSS Modeller 18 is a DM modeller
from IBM. So, it is not possible to diagnosis each classifier used in both
workbenches. Thus we can say that the ensemble method gives higher accuracy to
distinguish PD patient and healthy people. However, with the help of feature
selection strategy, those fields which can degrade the performance or
unimportant for modelling can be removed
to generate ensemble method with higher accuracy.


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