Machine Learning-Based Detection of Parkinson's Disease From Arabic Speech: A Cross-Linguistic Validation Study.

Early detection of Parkinson's disease (PD) through speech analysis offers significant clinical advantages, yet no validated tools exist for Arabic-speaking populations, representing a critical gap in global healthcare. Previous studies have relied on limited machine learning (ML) classifiers and voice attributes, which may introduce bias and hinder effective technique discovery. To address this, we developed an optimal PD prediction pipeline by testing multiple ML classifiers and feature extraction methods. We created the first Arabic PD speech dataset, comprising 40 subjects (17 with PD and 23 controls), and validated our methodology on an independent Spanish cohort of 100 subjects. Feature extraction included traditional, audio-to-text, and deep voice features from a pre-trained Whisper model. We employed feature selection and dimensionality reduction techniques to refine the dataset dimensions. Final features were assessed using twelve classifiers with leave-one-out and k-fold cross-validation for robust performance evaluation. Shapley additive explanations (SHAP) were utilized to determine feature importance as vocal biomarkers. Linear Discriminant Analysis achieved optimal performance with 90% accuracy, precision, recall, and F1-score using leave-one-out cross-validation. Linear Support Vector Classification also performed well, achieving 87.7% precision and 87.5% recall. When tested on the independent Spanish dataset, our methodology attained 83% accuracy, confirming cross-linguistic generalizability. SHAP analysis indicated that audio-to-text features provide contextual insights on fluency and coherence, while traditional features effectively capture acoustic variations. This study establishes the first validated Arabic PD speech classification system and demonstrates its universal applicability, laying the groundwork for global speech-based PD screening.