Abstract

Autism spectrum disorder (ASD) is a behavioural condition that affects the child's social interaction, communication, and behaviour. The early identification of ASD is critical for the effective and timely therapies. This study presents an enhanced prediction model for Autism Spectrum Disorder (ASD). It is based on facial features extracted from the face image. Mallat's multi-resolution algorithm is employed in this work for extracting facial features. Two distance based classifiers such as Euclidean Distance Classifier (EDC) and Absolute Distance Classifier (ADC) are employed for the ASD prediction. The proposed ASD prediction system is evaluated on face images of autistic and non-autistic children. The database is obtained from the Kaggle data repository. A total of 2940 facial images (1470 autistic and 1470 non-autistic) are employed for performance analysis. Experimental results show that the proposed ASD prediction system provides promising results with an accuracy of 97.01% by EDC and 96.87% by ADC classifiers.

Details

Title
An Enhanced Prediction Model For Autism Spectrum Disorder
Author
Amarnath, J Jegan 1 ; Meera, S 2 

 Associate Professor / Research Scholar, Department of.Computer Science And Engineering, SriSairam Engineering College / Vels Institute of Science, Technology & Advanced Studies, Chennai, Tamil Nadu, India. 
 Associate Professor, Department of.Computer Science And Engineering, Vels Institute of Science, Technology & Advanced Studies, Chennai, Tamil Nadu, India. 
Pages
1107-1112
Section
EDITORIAL
Publication year
2022
Publication date
Dec 2022
Publisher
Russian New University
e-ISSN
23047232
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2777086684
Copyright
© 2022. This work is published under http://www.cardiometry.net/issues (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.