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Combining Self-Organizing Maps and Decision Tree to Explain Diagnostic Decision Making in Attention-Deficit/Hyperactivity Disorder

Authors:
Anderson Silva
Luiz Carreiro
Mayara Silva
Maria Teixeira
Leandro Silva

Keywords: Self-Organizing Maps (SOM); Decision Tree; Attention Deficit/Hyperactivity Disorder (ADHD)

Abstract:
Attention-Deficit/Hyperactivity Disorder (ADHD) presents in children and adolescents as a persistent pattern of inattention, hyperactivity, and impulsivity that interferes with their development. Computational studies on ADHD focus on measures of brain activity of the participants and a few use standardized cognitive tests or behavioral inventories to assess objective indicators for diagnosis. The paper presents a computational proposal in which the combination of two artificial intelligence methods is used to aid the identification of diagnostic indicators for ADHD. The proposal is to combine a neural network of self-organizing maps to group factors from standardized tests and inventories, and a decision tree to classify the most relevant factors. The study included 127 children and adolescents from 6 to 16 years old, 48 with ADHD diagnosis and 79 without ADHD (control group). The most relevant result of the study was the strong contribution of the scores of the Inventory of Behaviors for Children and Adolescents in the diagnosis of the disorder.

Pages: 5 to 10

Copyright: Copyright (c) IARIA, 2021

Publication date: July 18, 2021

Published in: conference

ISSN: 2519-8653

ISBN: 978-1-61208-885-3

Location: Nice, France

Dates: from July 18, 2021 to July 22, 2021