Pre-diagnosis for Autism Spectrum Disorder Using Eye-Tracking and Machine Learning Techniques

Mustafa Mehmood, Hafeez Ullah Amin*, Po Ling Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

Abstract

This study explores the potential of utilizing machine learning in conjunction with gaze-tracking data to facilitate early or pre-diagnosis of ASD which can be cost-effective and beneficial to people with limited access to healthcare resources. A dataset comprising gaze-tracking information mapped onto images to differentiate between control subjects and autistic individuals is utilized and treated as an image classification problem. Two machine learning frameworks were employed for model training and testing: (1) a fast approach using principal component analysis (PCA) on the images followed by conventional machine learning algorithms such as ANN, Decision Tree, and support vector machines (SVM), which yielded an accuracy of 78% and an AUC of 0.82; and (2) a deep learning approach that involved a custom convolutional neural network (CNN) model, achieving an accuracy of 92% and an AUC of 0.96. Several transfer learning models were also evaluated, with the ResNet50 model providing the best results (accuracy: 0.86, AUC: 0.94). These findings demonstrate the viability of these methods for the pre-diagnosis of autism.

Original languageEnglish
Title of host publicationAdvances in Brain Inspired Cognitive Systems - 13th International Conference, BICS 2023, Proceedings
EditorsJinchang Ren, Amir Hussain, Iman Yi Liao, Rongjun Chen, Kaizhu Huang, Huimin Zhao, Xiaoyong Liu, Ping Ma, Thomas Maul
PublisherSpringer Science and Business Media Deutschland GmbH
Pages239-250
Number of pages12
ISBN (Print)9789819714162
DOIs
Publication statusPublished - 22 May 2024
Event13th International Conference on Brain Inspired Cognitive Systems, BICS 2023 - Kuala Lumpur, Malaysia
Duration: 5 Aug 20236 Aug 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14374 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Brain Inspired Cognitive Systems, BICS 2023
Country/TerritoryMalaysia
CityKuala Lumpur
Period5/08/236/08/23

Keywords

  • Autism Spectrum Disorder
  • Deep Learning
  • Feature Extraction
  • Mental Disease Diagnosis
  • Principal Component Analysis
  • Transfer Learning

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