Prediction of Parkinson’s disease using Machine Learning Techniques

Abstract

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 disease come into existence in either middle or late middle age. So this thesis focuses on the speech articulation difficulty symptoms of PD affected people and formulates the model using various machine learning techniques such as adaptive boosting, bagging, neural networks, support vector machine, decision tree, random forest and linear regression. Performance of these classifiers is evaluated using various metrics i.e. accuracy, receiver operating characteristic curve (ROC), Sensitivity, precision, specificity. At last, Boruta feature selection technique is used to find the most important features among all the feature to predict the Parkinson’s disease.