  Parkinson's disease (PD) is one of the major public health problems in the
world. It is a well-known fact that around one million people suffer from
Parkinson's disease in the United States whereas the number of people suffering
from Parkinson's disease worldwide is around 5 million. Thus, it is important
to predict Parkinson's disease in early stages so that early plan for the
necessary treatment can be made. People are mostly familiar with the motor
symptoms of Parkinson's disease, however, an increasing amount of research is
being done to predict the Parkinson's disease from non-motor symptoms that
precede the motor ones. If an early and reliable prediction is possible then a
patient can get a proper treatment at the right time. Nonmotor symptoms
considered are Rapid Eye Movement (REM) sleep Behaviour Disorder (RBD) and
olfactory loss. Developing machine learning models that can help us in
predicting the disease can play a vital role in early prediction. In this
paper, we extend a work which used the non-motor features such as RBD and
olfactory loss. Along with this the extended work also uses important
biomarkers. In this paper, we try to model this classifier using different
machine learning models that have not been used before. We developed automated
diagnostic models using Multilayer Perceptron, BayesNet, Random Forest and
Boosted Logistic Regression. It has been observed that Boosted Logistic
Regression provides the best performance with an impressive accuracy of 97.159
% and the area under the ROC curve was 98.9%. Thus, it is concluded that these
models can be used for early prediction of Parkinson's disease.
