Research Information System
Biomedical Signal Processing and Control, vol.103, 2025 (SCI-Expanded)
Background: Eye-tracking combined with machine learning (ML) has the potential to provide an objective and cost-effective method for identifying autism. However, current ML approaches that aim to identify Autism Spectrum Disorder (ASD) based on naturalistic web interactions have high variations on different pages and tasks. This inconsistency necessitates a more consistent approach since there is no clear pattern for identifying suitable web pages or tasks. In our previous study, we proposed using STA as a consistent approach, which demonstrated a higher mean accuracy than a relevant ML approach. However, our evaluation of STA was limited to a single dataset and a comparison with only one relevant ML approach. Objective: We aim to systematically evaluate the suitability of STA for autism identification using various datasets and compare its performance with other relevant ML approaches. Methods: We evaluated the accuracy of STA across various web pages, images, and tasks using three eye-tracking datasets and compared it to other ML approaches. Results: STA has demonstrated consistent accuracy across different web pages, images, and tasks, achieving a success rate of approximately 60%. STA has also outperformed other relevant ML approaches in terms of both the mean and standard deviation of accuracy rates on individual web pages, resulting in significantly higher results in most browsing tasks. Conclusion: STA is a consistent approach for identifying autism, as it achieves stable accuracy on various web pages, images, and tasks. However, further studies are necessary to improve its accuracy.