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 neuro-anatomical 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.

Authors and Affiliations

Jebapriya S, Shibin David, Jaspher W Kathrine, Naveen Sundar

Keywords

Related Articles

Deep Learning based Object Distance Measurement Method for Binocular Stereo Vision Blind Area

Visual field occlusion is one of the causes of urban traffic accidents in the process of reversing. In order to meet the requirements of vehicle safety and intelligence, a method of target distance measurement based on d...

Recent Approaches to Enhance the Efficiency of Ultra-Wide Band MAC Protocols

Ultra-wide band (UWB) is a promising radio technology to transmit huge data in short distances between different digital devices or between individual components of a personal computer. Due to the magnificent features of...

Development of a Novel Approach to Search Resources in IoT

Internet of Things (IoT) referred to interconnected the world of things like physical devices, cars, sensors, home appliances, actuators and machines embedded with software at any time, any location. The increasing numbe...

Towards Face Recognition Using Eigenface

This paper presents a face recognition system employing eigenface-based approach. The principal objective of this research is to extract feature vectors from images and to reduce the dimension of information. The method...

Examining the Impact of Feature Selection Methods on Text Classification

Feature selection that aims to determine and select the distinctive terms representing a best document is one of the most important steps of classification. With the feature selection, dimension of document vectors are r...

Download PDF file
  • EP ID EP646208
  • DOI 10.14569/IJACSA.2019.0100965
  • Views 93
  • Downloads 0

How To Cite

Jebapriya S, Shibin David, Jaspher W Kathrine, Naveen Sundar (2019). Support Vector Machine for Classification of Autism Spectrum Disorder based on Abnormal Structure of Corpus Callosum. International Journal of Advanced Computer Science & Applications, 10(9), 489-493. https://europub.co.uk/articles/-A-646208