An Extended Dataset of Extracted Acoustic Featuresfor Voice-Based Detection of Parkinson’s Disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder for which reliable, non-invasive biomarkers remain limited. Voice alterations, often present in the early stages of PD, are promising biomarkers for early detection. Existing public datasets remain a bottleneck to developing and validating early detection methodologies. We present an extended dataset integrating features from the Oxford Parkinson's Disease dataset and Iyer et al. This standardized dataset enables machine learning model training and evaluation, facilitates reproducibility, and supports exploration of novel voice-based biomarkers. We additionally provide analyses comparing feature distributions and model performance across individual and combined datasets, highlighting expected differences due to heterogeneous cohorts and feature extraction methods.