Fairness-Aware Partial-label Domain Adaptation for Voice Classification of Parkinson's and ALS
Abstract
Voice-based digital biomarkers can enable scalable, non-invasive screening and monitoring of Parkinson's disease (PD) and Amyotrophic Lateral Sclerosis (ALS). However, models trained on one cohort or device often fail on new acquisition settings due to cross-device and cross-cohort domain shift. This challenge is amplified in real-world scenarios with partial-label mismatch, where datasets may contain different disease labels and only partially overlap in class space. In addition, voice-based models may exploit demographic cues, raising concerns about gender-related unfairness, particularly when deployed across heterogeneous cohorts. To tackle these challenges, we propose a hybrid framework for unified three-class (healthy/PD/ALS) cross-domain voice classification from partially overlapping cohorts. The method combines style-based domain generalization with conditional adversarial alignment tailored to partial-label settings, reducing negative transfer. An additional adversarial gender branch promotes gender-invariant representations. We conduct a comprehensive evaluation across four heterogeneous sustained-vowel datasets, spanning distinct acquisition settings and devices, under both domain generalization and unsupervised domain adaptation protocols. The proposed approach is compared against twelve state-of-the-art machine learning and deep learning methods, and further evaluated through three targeted ablations, providing the first cross-cohort benchmark and end-to-end domain-adaptive framework for unified healthy/PD/ALS voice classification under partial-label mismatch and fairness constraints. Across all experimental settings, our method consistently achieves the best external generalization over the considered evaluation metrics, while maintaining reduced gender disparities. Notably, no competing method shows statistically significant gains in external performance.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel framework for voice classification of Parkinson's and ALS that effectively addresses challenges of domain adaptation and fairness. The comprehensive evaluation and innovative methodology contribute significantly to the fields of machine learning and medical AI.
Methodology
The proposed FairPDA framework integrates multiple advanced techniques such as style-based domain generalization, conditional adversarial alignment, and adversarial gender debiasing. This hybrid approach is well-structured and addresses the complex problem of partial-label domain adaptation while considering fairness in voice classification tasks. The methodology is innovative in its combination of techniques and is well-justified through the literature review, although it could benefit from clearer explanations of the specific contributions of each component.
Results & evaluation
The experiments are comprehensive, utilizing four heterogeneous datasets and comparing the proposed method against twelve state-of-the-art approaches. The evaluation metrics are appropriate for the task, including Balanced Accuracy, Matthews Correlation Coefficient, and fairness metrics. The results demonstrate that FairPDA consistently outperforms competing methods, although the absolute performance levels are moderate, indicating the difficulty of the task.
Reproducibility
The paper provides sufficient details regarding the methodology, datasets, and evaluation metrics, which facilitates reproducibility. However, the absence of a publicly available code repository or demo limits the ease with which others can replicate the findings.
Limitations
The study is limited by its focus on binary gender labels for fairness analysis, which restricts the scope of the fairness evaluation. Additionally, the performance metrics indicate that while FairPDA outperforms competitors, the overall accuracy remains moderate, suggesting that further improvements are needed for practical deployment.
Broader impact
The research has significant implications for the field of medical AI, particularly in the development of voice-based diagnostic tools for neurodegenerative diseases. By addressing fairness and domain adaptation, this work contributes to the ethical deployment of AI in healthcare, potentially leading to better patient outcomes across diverse populations. The paper presents a novel framework for voice classification of Parkinson's and ALS that effectively addresses challenges of domain adaptation and fairness. The comprehensive evaluation and innovative methodology contribute significantly to the fields of machine learning and medical AI.
Author of this paper? Send a correction.