Assessing gender fairness in EEG-based machine learning detection of Parkinson's disease: A multi-center study
This addresses fairness and bias in healthcare AI for Parkinson's disease detection, highlighting disparities that could exacerbate health inequalities, but it is incremental as it applies existing methods to analyze gender subgroups.
The study tackled the problem of gender fairness in machine learning detection of Parkinson's disease using EEG data, finding significant accuracy differences between males (80.5%) and females (63.7%) in a multi-center setting.
As the number of automatic tools based on machine learning (ML) and resting-state electroencephalography (rs-EEG) for Parkinson's disease (PD) detection keeps growing, the assessment of possible exacerbation of health disparities by means of fairness and bias analysis becomes more relevant. Protected attributes, such as gender, play an important role in PD diagnosis development. However, analysis of sub-group populations stemming from different genders is seldom taken into consideration in ML models' development or the performance assessment for PD detection. In this work, we perform a systematic analysis of the detection ability for gender sub-groups in a multi-center setting of a previously developed ML algorithm based on power spectral density (PSD) features of rs-EEG. We find significant differences in the PD detection ability for males and females at testing time (80.5% vs. 63.7% accuracy) and significantly higher activity for a set of parietal and frontal EEG channels and frequency sub-bands for PD and non-PD males that might explain the differences in the PD detection ability for the gender sub-groups.