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

Journal: BMC medical informatics and decision making
Published Date:

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.

Authors

  • Susanne Neufang
    Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.
  • Feifei Li
    School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 102488, China.
  • Atae Akhrif
    Institute of Biomedical Informatics, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.
  • Oya D Beyan
    Institute of Biomedical Informatics, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.