Abstract

Diagnosis of autism spectrum disorders (ASD) is a complex task, the solution of which usually depends on the experience of the physicians due to the lack of specific quantitative biomarkers. Machine learning and Deep Learning approaches are increasingly being used as a diagnostic tool for ASD classification, with the potential to improve discrimination validity among ASD and typically developing (TD) individuals. This paper describes the use of feature selection and two classification techniques to successfully distinguish between individuals with ASD and individuals without ASD, using data from a large resting-state functional magnetic resonance imaging (rs-fMRI) database.

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