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– ABSTRACT
Firstly, James Parkinson described Parkinson’s disease as a neurological
syndrome, it effects the central nervous system as a result the patients face the
issue in talking, strolling, tremor during motion. Parkinson’s disease patient
typically has a low-volume noise with a monotone quality, this system explores the
classification of audio signals feature dataset to diagnosis Parkinson’s disease
(PD), the classifiers we utilized in this system are from Machine Learning. The
significant classification models that are effectively utilized are (K-Nearest
Neighbor) KNN, Decision Tree, Logistic regression and eXtreme Gradient Boost
(XGboost). The performance analysis of the Parkinson’s disease datasets has a
variety of features. There are 21 features of all in which only 12 features play an
important role for predicting the best algorithm in the classification. The system
has achieved better result in predicting the PD patient healthy or not, XGBoost
provided the peak accuracy of 96% and the Matthews Correlation Coefficient
(MCC) of 89%. MDVP:Jitter(%), Jitter(Abs), MDVP:RAP, MDVP:PPQ, Jitter:DPP,
MDVP: Shimmer, MDVP:Shimmer(db), Shimmer APQ3,Shimmer APQ5, MDVP:APQ, Shimmer:DDA, NHR are the selected features from the dataset,which are important for the prediction to achieved high accuracy.
