A Comparative Analysis of Machine Learning classifiers for Dysphonia-based classification of Parkinson's Disease

摘要

Parkinson's Disease is the second most common neurogenerative disease that affects the nervous system. There is no permanent cure for this disease, so, its early diagnosis is important to improve the quality of living of Parkinson patients. The distortion of the voice is one of the first symptoms to appear in Parkinson patients. Therefore, comparison and classification plays an important role. In this paper, a comparison of various classification techniques is done to show the potential of each classifier. The various classification techniques include SVM (Linear, RBF, Polynomial), DT, RF, LR, KNN, NB, MLP, AdaBoost, and XGBoost. Three different types of feature selection techniques are also explored to reduce the dimensionality of the dataset without affecting the accuracy much. The three different feature selection techniques include mRMR, GA, and PCA. The potential of voice features in classification process is also shown.