In the current era, machine learning algorithms has taken a major role in classifying the disease and this has achieved a better result. In this work, Parkinson disease has been classified using machine learning algorithms. The novelty of this work is to implement the dimensionality reduction method before applying the classification algorithms. The dimensionality reduction is done through genetic algorithms in the form of feature selection. The selected features are then taken as input for performing the classification of the Parkinson disease. In this work, Parkinson disease dataset has been used for the prediction purpose and the machine learning algorithms has performed better with dimensionality reduction method. The gradient boosting algorithm has achieved higher accuracy of more than 97% during the classification with dimensionality reduction method.