An optimized Kernel Extreme Learning Machine for the classification of the autism spectrum disorder by using gaze tracking images

Autism spectrum disorder (ASD) is a lifelong neurological condition that affects how a person interacts and learns. The early and accurate diagnosis of ASD is vital to developing a comprehensive rehabilitation plan that improves the quality of life and the integration of the ASD person in the social, family, and work environment. However, the accurate diagnosis of ASD is usually affected since it is linked to the judgment of an expert, which produces biases related to the lack of objectivity. In consequence, several works have been dedicated to developing early detection techniques for ASD based on Machine Learning (ML) technologies and eye-tracking tools. The present work aims to introduce a new methodology for ASD classification, which uses Kernel Extreme Learning Machine (KELM), an objective dataset based on gaze tracking, feature extraction techniques, and data augmentation for training the model. In turn, to enhance the accuracy in ASD classification, the KELM model is optimized through the Giza Pyramids Construction (GPC) algorithm. The proposed approach includes pipeline data augmentation, dimensionality reduction, and a posterior normalization to classify ASD subjects accurately. Statistical tests and analyses were performed to validate the performance of the proposed methodology, resulting in an average accuracy of 98.8% in ASD classification.