Authors:
Romuald Carette
1
;
Mahmoud Elbattah
1
;
Federica Cilia
2
;
Gilles Dequen
1
;
Jean-Luc Guérin
1
and
Jérôme Bosche
1
Affiliations:
1
Laboratoire MIS, Université de Picardie Jules Verne, Amiens and France
;
2
Laboratoire CRP-CPO, Université de Picardie Jules Verne, Amiens and France
Keyword(s):
Autism Spectrum Disorder, Machine Learning, Eye-tracking, Scanpath.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Cloud Computing
;
Data Engineering
;
Data Management and Quality
;
Data Manipulation
;
Data Visualization
;
e-Health
;
Health Information Systems
;
Pattern Recognition and Machine Learning
;
Platforms and Applications
;
Sensor Networks
Abstract:
Autism spectrum disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. The early diagnosis of ASD is highly desirable, and there is a need for developing assistive tools to support the diagnosis process in this regard. This paper presents an approach to help with the ASD diagnosis with a particular focus on children at early stages of development. Using Machine Learning, our approach aims to learn the eye-tracking patterns of ASD. The key idea is to transform eye-tracking scanpaths into a visual representation, and hence the diagnosis can be approached as an image classification task. Our experimental results evidently demonstrated that such visual representations could simplify the prediction problem, and attained a high accuracy as well. With simple neural network models and a relatively limited dataset, our approach could realize a quite promising accuracy of classification (AUC > 0.9).