Beyond Fairness: Age-Harmless Parkinson's Detection via Voice
Yicheng Wang Xiaotian Han Leisheng Yu Na Zou
Parkinson's disease (PD), a neurodegenerative disorder, often manifests as
speech and voice dysfunction. While utilizing voice data for PD detection has
great potential in clinical applications, the widely used deep learning models
currently have fairness issues regarding different ages of onset. These deep
models perform well for the elderly group (age $>$ 55) but are less accurate
for the young group (age $\leq$ 55). Through our investigation, the discrepancy
between the elderly and the young arises due to 1) an imbalanced dataset and 2)
the milder symptoms often seen in early-onset patients. However, traditional
debiasing methods are impractical as they typically impair the prediction
accuracy for the majority group while minimizing the discrepancy. To address
this issue, we present a new debiasing method using GradCAM-based feature
masking combined with ensemble models, ensuring that neither fairness nor
accuracy is compromised. Specifically, the GradCAM-based feature masking
selectively obscures age-related features in the input voice data while
preserving essential information for PD detection. The ensemble models further
improve the prediction accuracy for the minority (young group). Our approach
effectively improves detection accuracy for early-onset patients without
sacrificing performance for the elderly group. Additionally, we propose a
two-step detection strategy for the young group, offering a practical risk
assessment for potential early-onset PD patients.