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Application of the decision tree model in ADHD screening

Published online by Cambridge University Press:  13 August 2021

L.R. Carreiro ,
M. Silva  and
M.C. Teixeira
L.R. Carreiro
Affiliation:
Developmental Disorders Graduate Program, Mackenzie Presbyterian University, São Paulo, Brazil
M. Silva*
Affiliation:
Developmental Disorders Graduate Program, Mackenzie Presbyterian University, São Paulo, Brazil
M.C. Teixeira
Affiliation:
Developmental Disorders Graduate Program, Mackenzie Presbyterian University, São Paulo, Brazil
*
*Corresponding author.

Abstract

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Introduction

Attention Deficit Hyperactivity Disorder (ADHD) is a Neurodevelopmental Disorder characterized by persistent pattern of inattention and hyperactivity / impulsivity. There is considerable difficulty in diagnosing ADHD, mainly to discriminate what could be symptoms arising from ADHD or typical age behaviors. The decision tree model is a statistical algorithm, a predictive model built with comparisons of values for a given objective that can be compared with other constant values, placing these variables in a database at hierarchical levels.

Objectives

This study aims to apply the decision tree model in directing the screening of ADHD complaints to analyze which cognitive and behavioral parameters would be better associations with ADHD accurate diagnosis

Methods

We used a database of research protocol with 202 children assessed with complaints of ADHD and a control group with 185 participants. Decision tree analyzed parameters selected from the cognitive instruments, such voluntary attention, Continuous Performance Test indexes, WCST indexes, Wechsler Intelligence indexes and behavioral scales from CBCL/6-1 and TRF/6-18.

Results

The highlighted results points to WCST index like: “Perseverative answers” and “Perseverative errors” and “learning to learn” joint to “CPT omissions” and behavioral scales as “CBCL ADHD”, and “CBCL Problems of Attention” produces accuracy of diagnosis discrimination from 84.7% to 60% in the precision of the decision tree.

Conclusions

The decision tree and machine learning approaches can be effective in directing the screening of typical ADHD complaints.

Disclosure

No significant relationships.

Keywords

ADHDBehavioral profileDecision treeNeuropsychological profile
Type
Abstract
Information
European Psychiatry , Volume 64 , Special Issue S1: Abstracts of the 29th European Congress of Psychiatry , April 2021 , pp. S754 - S755
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the European Psychiatric Association
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