Parkinson’s Disease Detection using Machine Learning Algorithms

Abstract

Abstract: The main goal of the study is to inspect the performance of the different Supervised Algorithms for the improving the Parkinson Disease diagnosis by detection. We have used Five machine learning techniques for the detection of Parkinson Disease datasets. KNN,LR,DT,NB and XGBoost were used for the prediction of Parkinson Disease. The Performance of the classifiers is evaluated via, precission, Accuracy, F1-Score, Recall and Support. Where after computing with different classifier we got result as KNN shows the accuracy level 96% for Parkinson Disease. XGBoost achieved the second highest classification accuracy of 91%. Moreover, in the terms of accuracy for analyzing the Parkinson Disease dataset ,NB achieved the lowest accuracy of 76%. In our study has emphasized the current Parkinson Disease research trends and scope in relational to clinical research fields by machine learning technique .That will be effective impact in field of Parkinson Disease.