Open Access

ARTICLE

Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach

Anupam Garg¹, Anshu Parashar¹, Dipto Barman², Sahil Jain³, Divya Singhal³, Mehedi Masud⁴, Mohamed Abouhawwash^5,6,*

1 Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India
2 School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
3 University Institute of Biotechnology, Chandigarh University, Mohali, India
4 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
5 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
6 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, MI, 48824, USA

* Corresponding Author: Mohamed Abouhawwash. Email:

(This article belongs to the Special Issue: Applications of Intelligent Systems in Computer Vision)

Computers, Materials & Continua 2022, 71(1), 1459-1471. https://doi.org/10.32604/cmc.2022.022170

Received 29 July 2021; Accepted 09 September 2021; Issue published 03 November 2021

Abstract

Autism Spectrum Disorder (ASD) is a developmental disorder whose symptoms become noticeable in early years of the age though it can be present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completely but can be reduced if detected early. An early diagnosis is hampered by the variation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergence of deep learning approaches in various fields, medical experts can be assisted in early diagnosis of ASD. It is very difficult for a practitioner to identify and concentrate on the major feature's leading to the accurate prediction of the ASD and this arises the need for having an automated approach. Also, presence of different symptoms of ASD traits amongst toddlers directs to the creation of a large feature dataset. In this study, we propose a hybrid approach comprising of both, deep learning and Explainable Artificial Intelligence (XAI) to find the most contributing features for the early and precise prediction of ASD. The proposed framework gives more accurate prediction along with the recommendations of predicted results which will be a vital aid clinically for better and early prediction of ASD traits amongst toddlers.

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Keywords

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