  In this modern era of overpopulation disease prediction is a crucial step in
diagnosing various diseases at an early stage. With the advancement of various
machine learning algorithms, the prediction has become quite easy. However, the
complex and the selection of an optimal machine learning technique for the
given dataset greatly affects the accuracy of the model. A large amount of
datasets exists globally but there is no effective use of it due to its
unstructured format. Hence, a lot of different techniques are available to
extract something useful for the real world to implement. Therefore, accuracy
becomes a major metric in evaluating the model. In this paper, a disease
prediction approach is proposed that implements a random forest classifier on
Parkinson's disease. We compared the accuracy of this model with the Principal
Component Analysis (PCA) applied Artificial Neural Network (ANN) model and
captured a visible difference. The model secured a significant accuracy of up
to 90%.
