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Evaluating Machine Learning Models for Prediction of Attention Deficit Hyperactivity Disorder among Autistic Individuals Using Genetic Data
Paper Details
Published: 2025/10/20
Journal: European Neuropsychopharmacology
Volume: 99
Number: 1
Container Title: Poster session
Paper Links
HTMLWith the change in classification systems in the last 12 years, it has become possible to make diagnoses of attention deficit hyperactivity disorder (ADHD) in autistic individuals. With this has come increasing awareness that autistic individuals with co-occurring ADHD experience additional challenges. Nevertheless, ADHD is a neurodevelopmental condition which is amenable to behavioural and pharmacological interventions, especially when identified early. Prediction algorithms often utilise regression methods which have limited decision boundaries. This study thus aimed to evaluate the utility of machine learning methods in combination with genetic data to predict co-occurring ADHD among autistic individuals.
Authors
Soumya Banerjee
Cambridge University
Senior Research Associate, Accelerate Programme
Ryan Daniels
University of Cambridge
Senior Machine Learning Engineer, Accelerate Programme
Niran Okewole
Christopher Bannon
Yuanjun Gu
Simon Baron-Cohen
Varun Warrier