Toward a fair, gender-debiased classifier for the diagnosis of attention deficit/hyperactivity disorder- a Machine-Learning based classification study.

Abstract

Background: Attention deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder. Gender disparities in the diagnosis of ADHD have been reported, suggesting that females tend to be diagnosed later in life than males are. The delayed diagnosis in females has been attributed to an inequality in the diagnostic criteria, failing to focus on the gender differences regarding symptomatology, comorbidity, and societal factors contributing to this disparity.

Methods: In this study, we introduced debiased classifiers for the diagnosis of ADHD via different bias mitigation algorithms of the AI Fairness 360 toolbox on a training dataset of 400 children and adolescents with and without ADHD (98 females, 25 ADHD patients, 73 typically developing females), a subsample of the Child Mind Institute dataset. Test data were acquired in an earlier study. Two datasets were used, one including personal characteristic features, scores of the clinical questionnaire Child Behavior Checklist, and wavelet variance coefficients as quantifiers of neural dynamics (fMRI), a second dataset included personal characteristic features, scores of the clinical questionnaire Child Behavior Checklist, and radiomic features of neural structure (sMRI).

Results: We found that the reweighed XGBoost model achieved the best accuracy and highest fairness in both datasets. Using model explanation, we showed how reweighing influenced feature importance at the global and local levels.

Conclusion: Based on methodological characteristics and insights from global and local model explana-tion, we discuss the reasons of these findings and conclude, that using the combination of bias mitigation and model explanation, improved classification models can be achieved.