Enhancing ADHD Diagnosis: Insights from Data- Driven Classification Approaches

Attention-Deficit/Hyperactivity Disorder
(ADHD) is a neurodevelopmental disorder marked by
persistent patterns of inattention, hyperactivity, and
impulsivity that disrupt normal development and daily
functioning. These symptoms typically appear in early
childhood and can significantly hinder social, academic,
and occupational performance. Although early and
accurate diagnosis is essential for effective management,
it remains challenging due to overlapping symptoms with
other conditions and variability in individual assessments.
This review paper examines the use of Deep Learning
(DL) algorithms in the early detection, classification, and
analysis of ADHD. This paper highlights how DL-based
approaches can overcome diagnostic limitations, enhance
accuracy, facilitate the development of personalized
treatment plans, and streamline clinical workflows.