Volume 11, Issue 3 (May & June 2020)                   BCN 2020, 11(3): 359-368 | Back to browse issues page


XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Rostami M, Farashi S, Khosrowabadi R, Pouretemad H. Discrimination of ADHD Subtypes Using Decision Tree on Behavioral, Neuropsychological, and Neural Markers. BCN. 2020; 11 (3) :359-368
URL: http://bcn.iums.ac.ir/article-1-1509-en.html
1- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran.
Abstract:  

Introduction: Attention-Deficit/Hyperactivity Disorder (ADHD) is a well-known neurodevelopmental disorder. Diagnosis and treatment of ADHD can often lead to a developmental trajectory toward positive results. The present study aimed at implementing the decision tree method to recognize children with and without ADHD, as well as ADHD subtypes. 
Methods: In the present study, the subjects included 61 children with ADHD (subdivided into ADHD-I (n=25), ADHD-H (n=14), and ADHD-C (n=22) groups) and 43 typically developing controls matched by IQ and age. The Child Behavior Checklist (CBCL), Integrated Visual And Auditory (IVA) test, and quantitative EEG during eyes-closed resting-state were utilized to evaluate the level of behavioral, neuropsychology, and electrophysiology markers using a decision tree algorithm, respectively.
Results: Based on the results, excellent classification accuracy (100%) was obtained to discriminate children with ADHD from the control group. Also, 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). Also, subtypes of ADHD can be distinguished from each other using behavioral, neuropsychiatric and electrophysiological parameters. The findings suggested that the 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 | Published: 2020/05/1

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


© 2020 All Rights Reserved | Basic and Clinical Neuroscience

Designed & Developed by : Yektaweb