Autism Spectrum Disorder Classification Using The Most Correlated Features

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social communication and repetitive behaviors. Diagnosis of ASD is currently based on behavioral assessments, which can be subjective and time-consuming. In this study, we aimed to develop a more objective and efficient method for ASD classification using functional Magnetic Resonance Imaging (fMRI) by extracting the most correlated features. We got fMRI data from Autism Brain Imaging Data Exchange (ABIDE I) that is intended to aid in developing computational models of autism and to facilitate the discovery of biomarkers for the disorder; we used this dataset with different machine and deep learning algorithms to classify individuals based on their brain activity patterns. Our results showed that using fMRI makes it possible to accurately classify individuals with and without ASD with high sensitivity and specificity. This study provides a promising direction for using fMRI, machine learning, and deep learning in the diagnostic process of ASD.