Exploring Gaze Pattern Differences Between Autistic and Neurotypical Children: Clustering, Visualisation, and Prediction
Journal:
arXiv
Published Date:
Sep 18, 2024
Abstract
Autism Spectrum Disorder (ASD) affects children's social and communication
abilities, with eye-tracking widely used to identify atypical gaze patterns.
While unsupervised clustering can automate the creation of areas of interest
for gaze feature extraction, the use of internal cluster validity indices, like
Silhouette Coefficient, to distinguish gaze pattern differences between ASD and
typically developing (TD) children remains underexplored. We explore whether
internal cluster validity indices can distinguish ASD from TD children.
Specifically, we apply seven clustering algorithms to gaze points and extract
63 internal cluster validity indices to reveal correlations with ASD diagnosis.
Using these indices, we train predictive models for ASD diagnosis. Experiments
on three datasets demonstrate high predictive accuracy (81\% AUC), validating
the effectiveness of these indices.
