Towards Automated Classification of Parkinson's Disease: Comparison of Machine Learning Methods using MRI and Acoustic Data

In this study, we focus on Parkinson's Disease (PD) classification and present a comparative analysis of prominent machine learning models using two distinct and independent modalities: Magnetic Resonance Imaging (MRI) and Acoustic data. Unlike many existing works that typically focus on a single modality, our research study provides performance evaluation on the performance of various algorithms on both MRI and Acoustic data. Through a detailed investigation, we provide an understanding of how different models perform when applied to each modality individually. Furthermore, our study extends beyond this comparative framework by introducing an ensemble approach aimed at enhancing the performance of machine learning models for PD classification using the acoustic data. Notably, our ensemble approach yields around a 12 % increase in overall performance.