Abstract:
Objective: The main goal of the study isto inspect the performance of three Supervised algorithms
for improving the Parkinson disease diagnosis by detection.
Methods: We used three machine learning techniques for the detection of Parkinson disease
datasets. SVM, KNN, and LR were used for prediction of Parkinson Disease. The performance of
the classifiers was evaluated via recall, precision, f 1 measure and accuracy.
Results: SVM shows the accuracy level 100% for Parkinson disease prediction. LR achieved the
second highest classification accuracy of 97%. Moreover, in the terms of accuracy for analyzing
Parkinson disease datasets, KNN achieved the worst performance (i.e. 60%).
Conclusion: Our finding showed that the SVM obtained the highest performance for analyzing
the Parkinson datasets. This study has emphasized the current Parkinson research trends and scope
in relation to clinical research fields by machine learning techniques. That will be an effective
impact in the field of Parkinson disease.
