Employing Machine Learning-Based Predictive Analytical Approaches to Classify Autism Spectrum Disorder Types. (10th January 2022)

• Record Type: Journal Article
• Authors: Hanif, Muhammad Kashif; Ashraf, Naba; Sarwar, Muhammad Umer; Adinew, Deleli Mesay; Yaqoob, Reehan
• Other Names: Du Sheng Academic Editor.
• Is Part Of: Complexity. Volume 2022(2022)
• Publication Date: 2022-01-10
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2022/8134018
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Autism spectrum disorder is an inherited long-living and neurological disorder that starts in the early age of childhood with complicated causes. Autism spectrum disorder can lead to mental disorders such as anxiety, miscommunication, and limited repetitive interest. If the autism spectrum disorder is detected in the early childhood, it will be very beneficial for children to enhance their mental health level. In this study, different machine and deep learning algorithms were applied to classify the severity of autism spectrum disorder. Moreover, different optimization techniques were employed to enhance the performance. The deep neural network performed better when compared with other approaches.