Implementing a Model to Detect Parkinson Disease using Machine Learning Classifiers

Abstract

Parkinson disease is a neurodegenerative disorder that affects nervous system and the root cause of it is falling rates of dopamine levels in the forebrain. The machine learning model is implemented to significantly improve diagnosis method of Parkinson disease. In this work it indicates that the ensemble techniques XGBoost classification (Extreme gradient boosting) algorithm achieved the high test accuracy rate (94.8%) compared to different classification algorithm.The performance of the methods has been assessed with a reliable dataset from UCI Machine learning repository