TY - GEN
T1 - Pre-diagnosis for Autism Spectrum Disorder Using Eye-Tracking and Machine Learning Techniques
AU - Mehmood, Mustafa
AU - Amin, Hafeez Ullah
AU - Chen, Po Ling
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024/5/22
Y1 - 2024/5/22
N2 - 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.
AB - 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.
KW - Autism Spectrum Disorder
KW - Deep Learning
KW - Feature Extraction
KW - Mental Disease Diagnosis
KW - Principal Component Analysis
KW - Transfer Learning
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U2 - 10.1007/978-981-97-1417-9_23
DO - 10.1007/978-981-97-1417-9_23
M3 - Conference proceeding (ISBN)
AN - SCOPUS:85195145692
SN - 9789819714162
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 239
EP - 250
BT - Advances in Brain Inspired Cognitive Systems - 13th International Conference, BICS 2023, Proceedings
A2 - Ren, Jinchang
A2 - Hussain, Amir
A2 - Liao, Iman Yi
A2 - Chen, Rongjun
A2 - Huang, Kaizhu
A2 - Zhao, Huimin
A2 - Liu, Xiaoyong
A2 - Ma, Ping
A2 - Maul, Thomas
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Brain Inspired Cognitive Systems, BICS 2023
Y2 - 5 August 2023 through 6 August 2023
ER -