An analytics-driven model for identifying autism spectrum disorder using eye tracking

The efficient and early detection of Autism Spectrum Disorder (ASD) is a critical objective in improving diagnosis and intervention outcomes. Various methods based on functional Magnetic Resonance Imaging (fMRI) and questionnaires have been explored, among which eye tracking is a promising approach. However, existing methods relying on eye tracking often restrict us to controlled environments, making things complicated and expensive. This study eliminates the requirement for specific parameters by concentrating just on eye movement data for ASD detection, therefore introducing a novel and user-friendly technique. Feature engineering is employed, encompassing preprocessing and extracting relevant gaze movement data. These properties are utilized in machine learning and deep learning model training with hyperparameter adjusting for optimization. Using the Saliency4ASD dataset and looking beyond its usual gaze focus, this study built a model that uses eye movement alone to identify ASD with about 81% accuracy. This safe, low-cost approach has the potential to provide simple technologies that enable early detection of ASD, hence allowing its accessibility to everyone. • Use eye movement data for effective and efficient Autism Spectrum Disorder (ASD) detection. • Build a model with 81% accuracy using eye tracking alone. • Apply feature engineering to preprocess and extract relevant gaze data. • Introduce a user-friendly, low-cost method for early ASD detection. • Compare machine learning and deep learning models accuracy.