An Ensemble Machine Learning Approach to Classify Parkinson’s Disease from Voice Signal

The progressive nature of Parkinson’s disease (PD) means that it eventually affects all areas of the nervous system and the body that the nervous system controls. The disorder usually shows up as tremors, but it can also make people stiff and slow their general movements. Early detection of PD can help take the necessary steps to prevent it, and machine learning (ML) is one of the best solutions nowadays. In this study, we classify PD from voice signals and find the best data balancing and dimensionality reduction techniques to improve the classification accuracy of the classifiers. We propose a stacking ensemble classifier, namely SVCXRF, that outperforms other classifiers. Logistic regression, random forest, K-nearest neighbour, and multilayer perceptron are used as models, and SMOTE, NearMiss, and SMOTETomek are used as data balancing techniques, and principal component analysis (PCA), independent component analysis (ICA), and truncated singular value decomposition (TSVD) are used as dimensionality reduction techniques. We check all the possible combinations for the ML algorithms. Empirical analysis shows that SMOTE and ICA as preprocessing pipelines and the proposed ensemble SVCXRF show maximum 97% accuracy, precision, recall, and f1-score to classify PD from the voice signal. In the best pipeline design, we classify PD using unsupervised ML method K-means clustering with low prediction accuracy. The proposed ensemble algorithm’s superiority is validated using another PD dataset. The tolerance test shows that the proposed ensemble classifier can predict accurately in any training–testing ratio.