Machine Learning Approaches to Identify and Classify ADHD: What We Know and What Still Needs Work
Ezra Lockhart N S* and Brittany Brzek C
Attention-Deficit/Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental disorder, affecting an estimated 5–10% of children globally, with symptoms frequently persisting into adulthood for approximately 60% of those individuals [1-6]. ADHD is characterized by a persistent pattern of inattention, hyperactivity, and impulsivity, leading to significant functional impairment across
scholastic, social, and occupational domains.
The official diagnosis of ADHD relies on phenomenological criteria, observable behavioral manifestations of symptoms, as specified by the latest edition of the Diagnostic and Statistical Manual of Mental Disorders [7]. However, this diagnostic process is frequently complex, costly, and time-consuming, often complicated further by the heterogeneity of ADHD presentations and frequent comorbidity with other psychiatric disorders. These factors can contribute to misdiagnosis or treatment delays [2,8,9]. In response, machine learning (ML) approaches have emerged as promising tools for the healthcare industry, aiming to expedite the diagnosis, identify risk factors, and
improve accuracy and timeliness of ADHD detection [2,10].
In this narrative review, we use a metaphor to bridge clinical expertise and technological tools to frame the discussion. We first outline evidence-based ADHD assessment workflows, then present a synthesis of ten recent ML studies, emphasizing methodologies, predictive features, and distinction between phenotype- and genotype-based approaches. Through the metaphor, we highlight potential points of integration and conclude with practical, ethical, and systemic considerations for ML in ADHD assessment, allowing you to evaluate the implications for clinical practice.
