# Assessing gender fairness in EEG-based machine learning detection of Parkinson's disease: A multi-center study

arXiv: https://arxiv.org/abs/2303.06376 · HTML: https://mlsanity.com/paper/2303.06376/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 11 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study](https://mlsanity.com/paper/2303.01389/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning](https://mlsanity.com/paper/2411.06624/) (2024)
- [On undesired emergent behaviors in compound prostate cancer detection systems](https://mlsanity.com/paper/2309.08381/) (2023)
- [Diagnosis of Parkinson's Disease Using EEG Signals and Machine Learning Techniques: A Comprehensive Study](https://mlsanity.com/paper/2405.00741/) (2024)

## Abstract (arXiv)

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.