An Attention-Based Hybrid Optimized Residual Memory Network (AHRML) Method for Autism Spectrum Disorder (ASD) Detection

Abstract

A neurological condition known as autism spectrum disorder (ASD) is marked by issues with speech, socializing, and monotonous activities. Even if genetics is the primary cause, prompt identification is vital, and using machine learning presents an intriguing opportunity to diagnose the condition more quickly and affordably. Even so, the specific problems of increased computational costs, extended execution times, and decreased efficacy concern the conventional approaches. In order to provide the highest level of disease prediction accuracy, the objective of the proposed study is to develop an automated tool for ASD detection that integrates a number of cutting-edge mining approaches. This study proposes a computer-aided and ultra-light framework called attention-based hybrid optimized residual memory network (AHRML) for accurate and efficient ASD detection. Here, a new hybridized Arithmetic Harris Hawks Optimizer is employed to minimize the dimensionality of features in order to streamline the disability identification process. Moreover, a sophisticated deep learning technique called attention-based residual term memory is developed to reliably and less frequently identify ASD from the provided data. The authors employed the ASD dataset to train and test the proposed model. The dataset includes demographic data (age and gender), behavioral characteristics (social skills and communication abilities), and ASD diagnosis data. In addition, a range of parameters were used to validate and test the proposed AHRML model’s performance using the popular ASD dataset.