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Discrimination of ADHD Subtypes Using Decision Tree on Behavioral, Neuropsychological, and Neural Markers
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...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Iranian Neuroscience Society
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7502189/ https://www.ncbi.nlm.nih.gov/pubmed/32963728 http://dx.doi.org/10.32598/bcn.9.10.115 |
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author | Rostami, Mohammad Farashi, Sajjad Khosrowabadi, Reza Pouretemad, Hamidreza |
author_facet | Rostami, Mohammad Farashi, Sajjad Khosrowabadi, Reza Pouretemad, Hamidreza |
author_sort | Rostami, Mohammad |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7502189 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Iranian Neuroscience Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-75021892020-09-21 Discrimination of ADHD Subtypes Using Decision Tree on Behavioral, Neuropsychological, and Neural Markers Rostami, Mohammad Farashi, Sajjad Khosrowabadi, Reza Pouretemad, Hamidreza Basic Clin Neurosci Research Paper 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. Iranian Neuroscience Society 2020 2020-05-01 /pmc/articles/PMC7502189/ /pubmed/32963728 http://dx.doi.org/10.32598/bcn.9.10.115 Text en Copyright© 2020 Iranian Neuroscience Society http://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License. |
spellingShingle | Research Paper Rostami, Mohammad Farashi, Sajjad Khosrowabadi, Reza Pouretemad, Hamidreza Discrimination of ADHD Subtypes Using Decision Tree on Behavioral, Neuropsychological, and Neural Markers |
title | Discrimination of ADHD Subtypes Using Decision Tree on Behavioral, Neuropsychological, and Neural Markers |
title_full | Discrimination of ADHD Subtypes Using Decision Tree on Behavioral, Neuropsychological, and Neural Markers |
title_fullStr | Discrimination of ADHD Subtypes Using Decision Tree on Behavioral, Neuropsychological, and Neural Markers |
title_full_unstemmed | Discrimination of ADHD Subtypes Using Decision Tree on Behavioral, Neuropsychological, and Neural Markers |
title_short | Discrimination of ADHD Subtypes Using Decision Tree on Behavioral, Neuropsychological, and Neural Markers |
title_sort | discrimination of adhd subtypes using decision tree on behavioral, neuropsychological, and neural markers |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7502189/ https://www.ncbi.nlm.nih.gov/pubmed/32963728 http://dx.doi.org/10.32598/bcn.9.10.115 |
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