Abstract
OBJECTIVE: With the increasing prevalence of Parkinson's disease (PD) and the development of PD-based acoustic recording databases, this study aims to evaluate the feasibility of using an ensemble-based machine learning (ML) approach to detect PD across diverse acoustic datasets. METHODS: We utilized three publicly available PD speech datasets-MIU (Sakar), UEX (Carrón), and UCI (Little)-to build ML models incorporating a hybrid preprocessing framework. This framework includes a scaling phase (using RobustScaler), a sampling phase (employing random oversampling (ROS), synthetic minority oversampling technique (SMOTE), and random undersampling (RUS)), and an ML classifier selection phase (featuring eXtreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost)). Performance was evaluated using accuracy, precision, recall, and F1-score metrics. Additionally, we conducted SHAP (SHapley Additive exPlanations) analysis to identify the most significant PD-related acoustic features. RESULTS: The optimal combination of preprocessing and classification techniques varied across datasets. However, the highest classification performance was generally achieved using RobustScaler for scaling, a combination of ROS, SMOTE, and RUS for sampling, and XGBoost or AdaBoost for classification. The best-performing model on the MIU dataset achieved accuracy of 97.37%, precision of 96.07%, and F1-score of 96.57%. The UEX and UCI datasets achieved perfect classification with 100% accuracy, precision, and recall. SHAP analysis revealed that Mel-frequency cepstral coefficients were consistently among the most influential PD-related acoustic features. CONCLUSIONS: Our findings confirm the feasibility of an ensemble-based approach for PD detection using acoustic recordings, highlighting the importance of dataset-specific preprocessing strategies. This study ranks impactful PD-related acoustic features, offering guidance for future voice-based PD screening tools.