Systematic Review of The Effectiveness of Machine Learning in Discriminating ADHD From ASD
DOI:
https://doi.org/10.54097/jjmkb320Keywords:
ADHD; ASD; machine learning; diagnosis; pediatric.Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that significantly impacts psychological, physical, and social functioning, especially in children. Traditional diagnostic methods—such as interviews, questionnaires, and Continuous Performance Tests (CPTs)—struggle to accurately distinguish ADHD from similar conditions, affecting treatment outcomes and quality of life. Recently, advancements in artificial intelligence (AI), particularly machine learning (ML), have been applied to improve ADHD classification. This review assesses the design and effectiveness of ML models in six studies for classifying ADHD and Autism Spectrum Disorder (ASD), highlighting promising approaches to enhance reliability and accuracy in these tasks.
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