Prediction of ADHD with Machine Learning: A Systematic Literature Review Abstract

Attention Deficit Hyperactivity Disorder (ADHD) affects approximately 13% of the child population; its diagnosis is based on clinical and behavioral assessments and psychometrics test results. Recently, Machine Learning (ML) has been used to detect various neuropsychiatric conditions, providing grea...

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Autores principales: Quintero-López, Catalina, Gil Vera, Víctor Daniel, Cerpa Bernal, Rafael Mauricio, Herrera Martínez, Marcelo
Formato: Artículo revista
Lenguaje:Español
Publicado: Universidad Nacional de Córdoba 2024
Materias:
ADHD
diagnosis
machine learning
predictive modeling
aprendizaje de máquinas
diagnóstico
modelado predictivo
TDAH
Acceso en línea:https://revistas.unc.edu.ar/index.php/racc/article/view/42221
Aporte de:
Revista Argentina de Ciencias del Comportamiento de Universidad Nacional de Córdoba
id I10-R363-article-42221
record_format ojs
spelling I10-R363-article-422212024-09-04T18:32:23Z Prediction of ADHD with Machine Learning: A Systematic Literature Review Abstract Predicción del TDAH con Aprendizaje de Máquinas: Revisión Sistemática de Literatura Quintero-López, Catalina Gil Vera, Víctor Daniel Cerpa Bernal, Rafael Mauricio Herrera Martínez, Marcelo ADHD diagnosis machine learning predictive modeling aprendizaje de máquinas diagnóstico modelado predictivo TDAH Attention Deficit Hyperactivity Disorder (ADHD) affects approximately 13% of the child population; its diagnosis is based on clinical and behavioral assessments and psychometrics test results. Recently, Machine Learning (ML) has been used to detect various neuropsychiatric conditions, providing greater diagnostic accuracy. The aim of this research was to conduct a systematic literature review (SLR) on the detection of ADHD in infants’ witch ML. Based on the PRISMA methodology, 30 publications were selected from Web of Science (WoS) and Scopus which met the eligibility criteria. This paper concludes that the most used technique for ADHD detection was Support Vector Machines, the best-performing model was obtained from psychometrics test. An accurate and early diagnosis of ADHD prevents long-term complications; emotional, academic, and social problems, as well as the development of an antisocial structure. El Trastorno por Déficit de Atención e Hiperactividad (TDAH) tiene una prevalencia estimada del 5.3% en la población mundial; su diagnóstico se basa en evaluaciones clínicas, conductuales y resultados de pruebas psicométricas. Recientemente el Aprendizaje de Máquinas (AM) se ha empleado para detectar diversas condiciones neuropsiquiátricas, brindando una mayor precisión diagnóstica. El objetivo de este estudio fue realizar una revisión sistemática de la literatura (RSL) sobre la detección del TDAH en infantes con AM. Con base en la metodología PRISMA, se seleccionaron 30 publicaciones de WebofScience (WoS) y Scopus, que cumplieron los criterios de elegibilidad. Se concluye que la técnica más empleada para detectar el TDAH fue Máquinas de Vectores de Soporte, el modelo con el mejor desempeño se obtuvo de pruebas psicométricas. Un diagnóstico certero y temprano del TDAH previene complicaciones a largo plazo, problemas emocionales, académicos y sociales, así como el desarrollo de una estructura antisocial. Universidad Nacional de Córdoba 2024-08-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.unc.edu.ar/index.php/racc/article/view/42221 10.32348/1852.4206.v16.n3.42221 Argentinean Journal of Behavioral Sciences; Vol. 16 No. 3 (2024): Revista Argentina de Ciencias del Comportamiento; 14-32 Revista Argentina de Ciencias del Comportamiento; Vol. 16 Núm. 3 (2024): Revista Argentina de Ciencias del Comportamiento; 14-32 1852-4206 10.32348/1852.4206.v16.n3 spa https://revistas.unc.edu.ar/index.php/racc/article/view/42221/46457 Derechos de autor 2024 Catalina Quintero-López, Víctor Daniel Gil Vera, Rafael Mauricio Cerpa Bernal, Marcelo Herrera Martínez http://creativecommons.org/licenses/by/4.0
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-363
container_title_str Revista Argentina de Ciencias del Comportamiento
language Español
format Artículo revista
topic ADHD
diagnosis
machine learning
predictive modeling
aprendizaje de máquinas
diagnóstico
modelado predictivo
TDAH
spellingShingle ADHD
diagnosis
machine learning
predictive modeling
aprendizaje de máquinas
diagnóstico
modelado predictivo
TDAH
Quintero-López, Catalina
Gil Vera, Víctor Daniel
Cerpa Bernal, Rafael Mauricio
Herrera Martínez, Marcelo
Prediction of ADHD with Machine Learning: A Systematic Literature Review Abstract
topic_facet ADHD
diagnosis
machine learning
predictive modeling
aprendizaje de máquinas
diagnóstico
modelado predictivo
TDAH
author Quintero-López, Catalina
Gil Vera, Víctor Daniel
Cerpa Bernal, Rafael Mauricio
Herrera Martínez, Marcelo
author_facet Quintero-López, Catalina
Gil Vera, Víctor Daniel
Cerpa Bernal, Rafael Mauricio
Herrera Martínez, Marcelo
author_sort Quintero-López, Catalina
title Prediction of ADHD with Machine Learning: A Systematic Literature Review Abstract
title_short Prediction of ADHD with Machine Learning: A Systematic Literature Review Abstract
title_full Prediction of ADHD with Machine Learning: A Systematic Literature Review Abstract
title_fullStr Prediction of ADHD with Machine Learning: A Systematic Literature Review Abstract
title_full_unstemmed Prediction of ADHD with Machine Learning: A Systematic Literature Review Abstract
title_sort prediction of adhd with machine learning: a systematic literature review abstract
description Attention Deficit Hyperactivity Disorder (ADHD) affects approximately 13% of the child population; its diagnosis is based on clinical and behavioral assessments and psychometrics test results. Recently, Machine Learning (ML) has been used to detect various neuropsychiatric conditions, providing greater diagnostic accuracy. The aim of this research was to conduct a systematic literature review (SLR) on the detection of ADHD in infants’ witch ML. Based on the PRISMA methodology, 30 publications were selected from Web of Science (WoS) and Scopus which met the eligibility criteria. This paper concludes that the most used technique for ADHD detection was Support Vector Machines, the best-performing model was obtained from psychometrics test. An accurate and early diagnosis of ADHD prevents long-term complications; emotional, academic, and social problems, as well as the development of an antisocial structure.
publisher Universidad Nacional de Córdoba
publishDate 2024
url https://revistas.unc.edu.ar/index.php/racc/article/view/42221
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first_indexed 2024-09-03T22:31:38Z
last_indexed 2025-02-05T22:15:00Z
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