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In this modern era of overpopulation disease prediction is a crucial step indiagnosing various diseases at an early stage. With the advancement of variousmachine learning algorithms, the prediction has become quite easy. However, thecomplex and the selection of an optimal machine learning technique for thegiven dataset greatly affects the accuracy of the model. A large amount ofdatasets exists globally but there is no effective use of it due to itsunstructured format. Hence, a lot of different techniques are available toextract something useful for the real world to implement. Therefore, accuracybecomes a major metric in evaluating the model. In this paper, a diseaseprediction approach is proposed that implements a random forest classifier onParkinson's disease. We compared the accuracy of this model with the PrincipalComponent Analysis (PCA) applied Artificial Neural Network (ANN) model andcaptured a visible difference. The model secured a significant accuracy of upto 90%.

PCA-RF: An Efficient Parkinson's Disease Prediction Model based on Random Forest Classification