Resumen
Autism spectrum disorder (ASD) is a developmental disorder that encompasses difficulties in communication (both verbal and non-verbal), social skills, and repetitive behaviors. The diagnosis of autism spectrum disorder typically involves specialized procedures and techniques, which can be time-consuming and expensive. The accuracy and efficiency of the diagnosis depend on the expertise of the specialists and the diagnostic methods employed. To address the growing need for early, rapid, cost-effective, and accurate diagnosis of autism spectrum disorder, there has been a search for advanced smart methods that can automatically classify the disorder. Machine learning offers sophisticated techniques for building automated classifiers that can be utilized by users and clinicians to enhance accuracy and efficiency in diagnosis. Eye-tracking scan paths have emerged as a tool increasingly used in autism spectrum disorder clinics. This methodology examines attentional processes by quantitatively measuring eye movements. Its precision, ease of use, and cost-effectiveness make it a promising platform for developing biomarkers for use in clinical trials for autism spectrum disorder. The detection of autism spectrum disorder can be achieved by observing the atypical visual attention patterns of children with the disorder compared to typically developing children. This study proposes a deep learning model, known as T-CNN-Autism Spectrum Disorder (T-CNN-ASD), that utilizes eye-tracking scans to classify participants into ASD and typical development (TD) groups. The proposed model consists of two hidden layers with 300 and 150 neurons, respectively, and underwent 10 rounds of cross-validation with a dropout rate of 20%. In the testing phase, the model achieved an accuracy of 95.59%, surpassing the accuracy of other machine learning algorithms such as random forest (RF), decision tree (DT), K-Nearest Neighbors (KNN), and multi-layer perceptron (MLP). Furthermore, the proposed model demonstrated superior performance when compared to the findings reported in previous studies. The results demonstrate that the proposed model can accurately classify children with ASD from those with TD without human intervention.