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1- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran.


Introduction: Attention Deficit/Hyperactivity Disorder (ADHD) is considered as a well-known neurodevelopmental disorder. Diagnosis and treatment of ADHD can often lead to a developmental trajectory toward positive results. The present study aimed to implement a decision tree method to recognize children with and without ADHD, as well as ADHD subtypes.
Methods: In the present study, the subjects included 77 children with ADHD (subdivided into ADHD-I, N= 25; ADHD-H, N=14, and ADHD-C, N=22) and 43 typically developing children matched by IQ and age. Child Behavior Checklist (CBCL), Integrated Visual, and Auditory Test (IVA) and quantitative EEG during eye closed resting state were utilized to evaluate the level of behavioral, neuropsychology and electrophysiology respectively using a decision tree algorithm.
Results: Based on the results, excellent classification accuracy (100%) was obtained to discriminate children with ADHD from the control group. In addition, the ADHD subtypes including combined, inattention and hyperactive/impulsive subtypes were recognized from others with an accuracy of 80.41%, 84.17%, and 71.46%, respectively.
Conclusion: Our results showed that children with ADHD can be recognized from the healthy controls based on the neuropsychological data (sensory-motor parameters of IVA). In addition, subtypes of ADHD can also be distinguished from each other using behavioral, neuropsychiatric and electrophysiological parameters. The findings suggest a decision tree method may present an efficient and accurate diagnostic tool for the clinicians.
Type of Study: Original | Subject: Cellular and molecular Neuroscience
Received: 2019/05/23 | Accepted: 2019/07/28

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