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.