Early ASD Screening from Eye-Tracking Data: A Comparative Study of Classical and Deep Learning Models

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects one throughout life. Early treatment response is strengthened by timely diagnosis of Autism Spectrum Disorder (ASD) although the current screening algorithms rely on behavioral assessment, which is both resource consuming and subjective. The use of eye-tracking needs to provoke characteristic variation in social attention, and in the former studies, the classical and deep learning models are tested, but comparisons are often limited by limited participants, confounding tasks, and imprecise measurements. This paper bridges this gap with a question about whether an integrated and reproducible pipeline can help make a fair comparison of classical and deep methods to screening ASD and reduce false negatives in practice. We use our eye-tracking signal analysis as the standardized workflow data cleaning, data normalization, class balancing and statistical validation mode (ANOVA and Correlation) and test four baselines (SVM, Random Forest, MLP, LSTM) using the same splits and measurements and test a stacking ensemble with a recall-oriented one. Random Forest has the highest total accuracy and AUC, LSTM models differentiates temporal gazes effectively and the ensemble maximizes recall, which minimizes the risk of false negative cases. The results obtained in this research imply whether the combination of classical and deep learning models is done cautiously, a viable line of constructing supportive screening tools can be offered. Reproducible implementation, sensitivity of sensitive information, and cost-aware computation are also in-house priorities that enable us to be responsible adopters of the technology in the healthcare facilities.