Copertina

Identifying neuroanatomical and behavioral features for autism spectrum disorder diagnosis in children using machine learning

Salvato in:
Pubblicato in:PLoS One vol. 17, no. 7 (Jul 2022), p. e0269773
Autore principale: Han, Yu
Altri autori: Rizzo, Donna M, Hanley, John P, Coderre, Emily L, Prelock, Patricia A
Pubblicazione:
Public Library of Science
Soggetti:
Cognition
Datasets
Scanners
Algorithms
Autism
Medical imaging
Neurodevelopmental disorders
Brain architecture
Feature selection
Diagnosis
Machine learning
Cortex (frontal)
Automation
Cortex (cingulate)
Prediction models
Learning algorithms
Cortex (parietal)
Evolutionary algorithms
Children
Anatomy
Genetic algorithms
Magnetic resonance imaging
Biomarkers
Neural networks
Classification
Neuroimaging
Medical diagnosis
Temporal lobe
Theory of mind
Social
Accesso online:Citation/Abstract
Full Text
Full Text - PDF
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
Abstract:Autism spectrum disorder (ASD) is a neurodevelopmental disorder that can cause significant social, communication, and behavioral challenges. Diagnosis of ASD is complicated and there is an urgent need to identify ASD-associated biomarkers and features to help automate diagnostics and develop predictive ASD models. The present study adopts a novel evolutionary algorithm, the conjunctive clause evolutionary algorithm (CCEA), to select features most significant for distinguishing individuals with and without ASD, and is able to accommodate datasets having a small number of samples with a large number of feature measurements. The dataset is unique and comprises both behavioral and neuroimaging measurements from a total of 28 children from 7 to 14 years old. Potential biomarker candidates identified include brain volume, area, cortical thickness, and mean curvature in specific regions around the cingulate cortex, frontal cortex, and temporal-parietal junction, as well as behavioral features associated with theory of mind. A separate machine learning classifier (i.e., k-nearest neighbors algorithm) was used to validate the CCEA feature selection and for ASD prediction. Study findings demonstrate how machine learning tools might help move the needle on improving diagnostic and predictive models of ASD.
ISSN:1932-6203
DOI:10.1371/journal.pone.0269773
Fonte:Health & Medical Collection