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A Machine Learning Approach in Autism Spectrum Disorders: From Sensory Processing to Behavior Problems

Atypical sensory processing described in autism spectrum disorders (ASDs) frequently cascade into behavioral alterations: isolation, aggression, indifference, anxious/depressed states, or attention problems. Predictive machine learning models might refine the statistical explorations of the associat...

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Autores principales: Alateyat, Heba, Cruz, Sara, Cernadas, Eva, Tubío-Fungueiriño, María, Sampaio, Adriana, González-Villar, Alberto, Carracedo, Angel, Fernández-Delgado, Manuel, Fernández-Prieto, Montse
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9126208/
https://www.ncbi.nlm.nih.gov/pubmed/35615066
http://dx.doi.org/10.3389/fnmol.2022.889641
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author Alateyat, Heba
Cruz, Sara
Cernadas, Eva
Tubío-Fungueiriño, María
Sampaio, Adriana
González-Villar, Alberto
Carracedo, Angel
Fernández-Delgado, Manuel
Fernández-Prieto, Montse
author_facet Alateyat, Heba
Cruz, Sara
Cernadas, Eva
Tubío-Fungueiriño, María
Sampaio, Adriana
González-Villar, Alberto
Carracedo, Angel
Fernández-Delgado, Manuel
Fernández-Prieto, Montse
author_sort Alateyat, Heba
collection PubMed
description Atypical sensory processing described in autism spectrum disorders (ASDs) frequently cascade into behavioral alterations: isolation, aggression, indifference, anxious/depressed states, or attention problems. Predictive machine learning models might refine the statistical explorations of the associations between them by finding out how these dimensions are related. This study investigates whether behavior problems can be predicted using sensory processing abilities. Participants were 72 children and adolescents (21 females) diagnosed with ASD, aged between 6 and 14 years (M = 7.83 years; SD = 2.80 years). Parents of the participants were invited to answer the Sensory Profile 2 (SP2) and the Child Behavior Checklist (CBCL) questionnaires. A collection of 26 supervised machine learning regression models of different families was developed to predict the CBCL outcomes using the SP2 scores. The most reliable predictions were for the following outcomes: total problems (using the items in the SP2 touch scale as inputs), anxiety/depression (using avoiding quadrant), social problems (registration), and externalizing scales, revealing interesting relations between CBCL outcomes and SP2 scales. The prediction reliability on the remaining outcomes was “moderate to good” except somatic complaints and rule-breaking, where it was “bad to moderate.” Linear and ridge regression achieved the best prediction for a single outcome and globally, respectively, and gradient boosting machine achieved the best prediction in three outcomes. Results highlight the utility of several machine learning models in studying the predictive value of sensory processing impairments (with an early onset) on specific behavior alterations, providing evidences of relationship between sensory processing impairments and behavior problems in ASD.
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spelling pubmed-91262082022-05-24 A Machine Learning Approach in Autism Spectrum Disorders: From Sensory Processing to Behavior Problems Alateyat, Heba Cruz, Sara Cernadas, Eva Tubío-Fungueiriño, María Sampaio, Adriana González-Villar, Alberto Carracedo, Angel Fernández-Delgado, Manuel Fernández-Prieto, Montse Front Mol Neurosci Molecular Neuroscience Atypical sensory processing described in autism spectrum disorders (ASDs) frequently cascade into behavioral alterations: isolation, aggression, indifference, anxious/depressed states, or attention problems. Predictive machine learning models might refine the statistical explorations of the associations between them by finding out how these dimensions are related. This study investigates whether behavior problems can be predicted using sensory processing abilities. Participants were 72 children and adolescents (21 females) diagnosed with ASD, aged between 6 and 14 years (M = 7.83 years; SD = 2.80 years). Parents of the participants were invited to answer the Sensory Profile 2 (SP2) and the Child Behavior Checklist (CBCL) questionnaires. A collection of 26 supervised machine learning regression models of different families was developed to predict the CBCL outcomes using the SP2 scores. The most reliable predictions were for the following outcomes: total problems (using the items in the SP2 touch scale as inputs), anxiety/depression (using avoiding quadrant), social problems (registration), and externalizing scales, revealing interesting relations between CBCL outcomes and SP2 scales. The prediction reliability on the remaining outcomes was “moderate to good” except somatic complaints and rule-breaking, where it was “bad to moderate.” Linear and ridge regression achieved the best prediction for a single outcome and globally, respectively, and gradient boosting machine achieved the best prediction in three outcomes. Results highlight the utility of several machine learning models in studying the predictive value of sensory processing impairments (with an early onset) on specific behavior alterations, providing evidences of relationship between sensory processing impairments and behavior problems in ASD. Frontiers Media S.A. 2022-05-09 /pmc/articles/PMC9126208/ /pubmed/35615066 http://dx.doi.org/10.3389/fnmol.2022.889641 Text en Copyright © 2022 Alateyat, Cruz, Cernadas, Tubío-Fungueiriño, Sampaio, González-Villar, Carracedo, Fernández-Delgado and Fernández-Prieto. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Neuroscience
Alateyat, Heba
Cruz, Sara
Cernadas, Eva
Tubío-Fungueiriño, María
Sampaio, Adriana
González-Villar, Alberto
Carracedo, Angel
Fernández-Delgado, Manuel
Fernández-Prieto, Montse
A Machine Learning Approach in Autism Spectrum Disorders: From Sensory Processing to Behavior Problems
title A Machine Learning Approach in Autism Spectrum Disorders: From Sensory Processing to Behavior Problems
title_full A Machine Learning Approach in Autism Spectrum Disorders: From Sensory Processing to Behavior Problems
title_fullStr A Machine Learning Approach in Autism Spectrum Disorders: From Sensory Processing to Behavior Problems
title_full_unstemmed A Machine Learning Approach in Autism Spectrum Disorders: From Sensory Processing to Behavior Problems
title_short A Machine Learning Approach in Autism Spectrum Disorders: From Sensory Processing to Behavior Problems
title_sort machine learning approach in autism spectrum disorders: from sensory processing to behavior problems
topic Molecular Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9126208/
https://www.ncbi.nlm.nih.gov/pubmed/35615066
http://dx.doi.org/10.3389/fnmol.2022.889641
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