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

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 ident

SUBMITTER: Han Y 

PROVIDER: S-EPMC9262216 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature