Hybrid Machine Learning Framework for Multistage Parkinson’s Disease Classification Using Acoustic Features of Sustained Korean Vowels

Abstract

Recent research has achieved a great classification rate for separating healthy people from those with Parkinson’s disease (PD) using speech and the voice. However, these studies have primarily treated early and advanced stages of PD as equal entities, neglecting the distinctive speech impairments and other symptoms that vary across the different stages of the disease. To address this limitation, and improve diagnostic precision, this study assesses the selected acoustic features of dysphonia, as they relate to PD and the Hoehn and Yahr stages, by combining various preprocessing techniques and multiple classification algorithms, to create a comprehensive and robust solution for classification tasks. The dysphonia features extracted from the three sustained Korean vowels /아/(a), /이/(i), and /우/(u) exhibit diversity and strong correlations. To address this issue, the analysis of variance F-Value feature selection classifier from scikit-learn was employed, to identify the topmost relevant features. Additionally, to overcome the class imbalance problem, the synthetic minority over-sampling technique was utilized. To ensure fair comparisons, and mitigate the influence of individual classifiers, four commonly used machine learning classifiers, namely random forest (RF), support vector machine (SVM), k-nearest neighbor (kNN), and multi-layer perceptron (MLP), were employed. This approach enables a comprehensive evaluation of the feature extraction methods, and minimizes the variance in the final classification models. The proposed hybrid machine learning pipeline using the acoustic features of sustained vowels efficiently detects the early and mid-advanced stages of PD with a detection accuracy of 95.48%, and with a detection accuracy of 86.62% for the 4-stage, and a detection accuracy of 89.48% for the 3-stage classification of PD. This study successfully demonstrates the significance of utilizing the diverse acoustic features of dysphonia in the classification of PD and its stages.

Ensemble Machine Learning Approach for Parkinson’s Disease Detection Using Speech Signals

“…Aiming to highlight the importance of using acoustic features in the detection of PD at its early and mid-advance stages, ref. [17] integrated feature selection methods with ML classifiers. In addition to detecting early and mid-advanced stages of PD with an accuracy of 95.4%, their hybrid model also detected stage 3 and stage 4 of PD with an accuracy of 89.48% and 86.62%, respectively.…”

“…A DL hybrid model that combines CNN and LSTM, as suggested by [21], attained an accuracy of 93.51%. With the aim of combining feature extraction methods with ML classifiers, two hybrid models introduced by [17,18] achieved an accuracy of 95.48% and 95.58%, respectively. With a superior accuracy of 96% and an AUC score of 0.99, the proposed model thrives across well-known performance metrics, qualifying as an effective and innovative solution for the detection of PD using speech signals.…”