Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Machine Learning

Tianhua Chen, Grigoris Antoniou, Marios Adamou, Ilias Tachmazidis, Pan Su

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that includes symptoms such as inattentiveness, hyperactivity and impulsiveness. It is considered as an important public health issue, and prevalence of diagnosis has increased as awareness of the disease grew over the past years. Supply of specialist medical experts has not kept pace with the increasing demand for assessment, both due to financial pressures on health systems and the difficulty to train new experts, resulting in growing waiting lists. Patients are not being treated quickly enough causing problems in other areas of health systems (e.g. increased GP visits, increased risk of self-harm and accidents) and more broadly (e.g. time off work, relationship problems). Advances in machine learning make it possible to attempt to diagnose ADHD based on the analysis of relevant data, and this could inform clinical practice. This paper reports on findings related to the mental health services of a specialist Trust within the UK’s National Health Service (NHS). The analysis studied data of adult patients who underwent diagnosis over the past few years, and developed a diagnostic model for ADHD in adults. The results demonstrate that it is indeed possible to correctly diagnose ADHD patients with promising statistical accuracy.
Original languageEnglish
Pages (from-to)657-669
Number of pages13
JournalApplied Artificial Intelligence
Issue number9
Early online date2 Jun 2021
Publication statusPublished - 1 Sep 2021


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