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Parkinson’s disease (PD) is one of the major public health disease in the world which is progressively increasing day by day and had its effect on many countries. Thus, it is very important to predict it in early age which has been challenging task among researchers because the symptoms of the disease come into existence in either middle or late middle age. So this Project focuses on the Spiral Test difficulty symptoms of PD affected people and formulates the model using various machine learning techniques such as adaptive boosting,Recurrent Neural Network (RNN), Convolutional Deep Neural Networks, support vector machine, decision tree, Convolutional Neural Networks and linear regression. Performance of these classifiers is evaluated using metric such as. accuracy, Receiver Operating Characteristic curve (ROC), Sensitivity, precision. At last, the Feature selection technique is used to find the most important features among all the features to predict Parkinson’s disease.
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