Support Vector Machine for Classification of Autism Spectrum Disorder based on Abnormal Structure of Corpus Callosum

Abstract

Autism Spectrum Disorders (ASD) is quite difficult to diagnose using traditional methods. Early prediction of Autism Spectrum Disorders enhances the in general psychological well-being of the child. These days, the research on Autism Spectrum Disorder is performed at a very high pace than earlier days due to increased rate of ASD affected people. One possible way of diagnosing ASD is through behavioral changes of children at the early ages. Structural imaging ponders point to disturbances in various mind regions, yet the exact neuroanatomical nature of these interruptions stays misty. Portrayal of cerebrum structural contrasts in children with ASD is basic for advancement of biomarkers that may in the long run be utilized to enhance analysis and screen reaction to treatment. In this examination we use machine figuring out how to decide a lot of conditions that together end up being prescient of Autism Spectrum Disorder. This will be of an extraordinary use to doctors, making a difference in identifying Autism Spectrum Disorder at a lot prior organize.

Classification of Coherence Indices Extracted from EEG Signals of Mild and Severe Autism

“…Brain images have shown abnormalities in brain and head size and limbic and cerebellar structure in autistic patients [10]. Magnetic resonance imaging (MRI) studies have shown that autistic patients have serious disturbances in the functional connectivity between different brain regions [11,12]. fMRI and diffusion tensor imaging (DTI) studies also showed that patients with autism had reduced long-range functional connectivity at rest and during different executive cognitive tasks [13].…”