Are the Eyes a Mirror of Autism? Explainable AI-Based Biomarker Discovery for Diagnosis

Autism spectrum disorder (ASD) presents significant difficulties in diagnosis due to its nature and diversity of symptoms. Traditional diagnostic methods rely on comprehensive clinical evaluations and complex diagnostic processes. Recent advances in machine learning (ML) and explainable artificial intelligence (XAI) offer promising models that help improve diagnostic accuracy and efficiency in ASD. XAI methods provide explainability by determining feature importance and indicating the extent to which features impact the outcome. In this study, we use children’s eye-tracking data and propose using this capability of XAI as a feature selection (FS) method in ASD diagnosis. Specifically, we aim to compare the performance of XAI-based and traditional FS methods individually and explore the benefits of combining these approaches. The goal is to enhance the diagnostic accuracy of ML models while simultaneously identifying significant features—a process referred to as biomarker discovery—through the proposed system. This system enables the identification of critical features that differentiate children with ASD from their typically developing (TD) peers, offering valuable insights to clinicians for improved diagnostic practices. The results demonstrate that the XAI-based FS method improves model performance compared to traditional feature selection techniques, with the highest AUC score of 0.78 achieved by combining feature sets from both methods. Furthermore, the XAI approach successfully identified four candidate biomarkers critical to ASD diagnosis, which were not selected by traditional methods. These findings underscore the potential of XAI techniques not only to boost model accuracy but also to uncover new diagnostic insights. By combining the XAI-based FS method with traditional approaches, this research contributes to advancing diagnostic practices in ASD, identifying new biomarkers, and enhancing clinical decision-making in pediatric neurodevelopmental disorders.