An enhanced prediction model for autism spectrum disorder

Cardiometry

article

J. Jegan Amarnath¹  S. Meera² 

[1] Department of .Computer Science And Engineering, SriSairam Engineering College / Vels Institute of Science;Department of .Computer Science And Engineering, Vels Institute of Science

关键词: Autism spectrum disorder;    Computer aided diagnosis;    Machine learning;    Image based diagnosis;    Prediction system;   

DOI  :  10.18137/cardiometry.2022.25.11071112

学科分类：环境科学（综合）

来源: Russian New University

【 摘 要 】

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.

【 授权许可】

CC BY