title: Diagnostic Classification of ADHD Versus Control: Support Vector Machine Classification Using Brief Neuropsychological Assessment.
abstract: Objective:Common methods for clinical diagnosis include clinical interview, behavioral questionnaires, and neuropsychological assessment. These methods rely on clinical interpretation and have variable reliability, sensitivity, and specificity. The goal of this study was to evaluate the utility of machine learning in the prediction and classification of children with ADHD-Combined presentation (ADHD-C) using brief neuropsychological measures (d2 Test of Attention, Children with ADHD-C and typically developing control children completed semi-structured clinical interviews and measures of attention/concentration and parents completed symptom severity questionnaires.Method:We used a forward feature selection method to identify the most informative neuropsychological features for support vector machine (SVM) classification and a decision tree model to derive a rule-based model.Results:The SVM model yielded excellent classification accuracy (100%) of individual children with and without ADHD (1.0). Decision tree algorithms identified individuals with and without ADHD-C with 100% sensitivity and specificity.Conclusion:This study observed highly accurate statistical diagnostic classification, at the individual level, in a sample of children with ADHD-C. The findings suggest data-driven behavioral algorithms based on brief neuropsychological data may present an efficient and accurate diagnostic tool for clinicians.
authors.affiliation: University of Washington School of Medicine, Seattle, USA.|Seattle Children's Hospital, Seattle, USA.
authors.forename: Jesse C
authors.initials: JC
authors.lastname: Bledsoe
authors.affiliation: University of Washington, Department of Industrial and Systems Engineering, Seattle, USA.
authors.forename: Cao
authors.initials: C
authors.lastname: Xiao
authors.affiliation: University of Washington, Department of Industrial and Systems Engineering, Seattle, USA.|University of Washington, Department of Radiology, Seattle, USA.
authors.forename: Art
authors.initials: A
authors.lastname: Chaovalitwongse
authors.affiliation: University of Washington, Department of Radiology, Seattle, USA.
authors.forename: Sonya
authors.initials: S
authors.lastname: Mehta
authors.affiliation: University of Washington, Department of Radiology, Seattle, USA.|University of Washington, Department of Neurology, Seattle, USA.
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Grabowski
authors.affiliation: University of Minnesota Medical School, Minneapolis, USA.
authors.forename: Margaret
authors.initials: M
authors.lastname: Semrud-Clikeman
authors.affiliation: University of Texas Health and Science Center, San Antonio, USA.
authors.forename: Steven
authors.initials: S
authors.lastname: Pliszka
authors.affiliation: University of Washington School of Medicine, Seattle, USA.|Seattle Children's Hospital, Seattle, USA.
authors.forename: David
authors.initials: D
authors.lastname: Breiger
mesh_terms: D001289:Attention Deficit Disorder with Hyperactivity / Q000175:diagnosis; D002648:Child; D006801:Humans; D000069550:Machine Learning; D009483:Neuropsychological Tests; D015203:Reproducibility of Results; D060388:Support Vector Machine
keywords: ADHD; decision making; neuropsychological functioning