Clinical Report: A Federated Learning Approach for Explainable Detection of Parkinson's Disease
Overview
This study investigates the use of federated learning for detecting Parkinson's disease through voice analysis. It addresses the limitations of single-dataset evaluations and explores the interpretability and privacy of machine learning models in clinical settings.
Background
Parkinson's disease is a prevalent neurodegenerative disorder that requires early identification for effective management. Traditional diagnostic methods are often resource-intensive and subjective, highlighting the need for automated tools for screening processes. Voice-based analysis has emerged as a promising non-invasive method for detecting Parkinson's disease, yet challenges remain in ensuring model generalization across diverse datasets.
Data Highlights
No numerical data available in the provided source material.
Key Findings
High classification accuracy in single-dataset evaluations may not translate to robust performance in real-world scenarios.
Dataset heterogeneity significantly affects the generalization capability of voice-based detection models.
Frequency-based (jitter) and amplitude-based (shimmer) features have varying contributions to detection reliability.
Explainable artificial intelligence techniques can provide meaningful interpretations under cross-dataset conditions.
Federated learning can achieve competitive generalization performance while preserving data privacy.
Clinical Implications
The findings indicate that reliance on single-dataset evaluations may lead to overestimated model performance in clinical applications.
Conclusion
This study highlights the importance of generalization and interpretability in developing machine learning models for Parkinson's disease detection.
