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 subsamp

SUBMITTER: Neufang S 

PROVIDER: S-EPMC12326834 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature