Trailblazing ASD Behavioral Research: Leveraging Modified MLP with Cross-Weighted Attention Mechanism at the Forefront

Abstract

Autism spectrum disorder (ASD) is an enduring and intricate condition that encompasses issues with behavior and communication. This disability has become common among several individuals globally. To ensure that all the autistic individuals get the correct support and care throughout their lifetime, it is significant that the autism signs are realized and the condition is spotted in the initial phase itself to avoid further complications. However, the process of ASD diagnosis is time-consuming, with expensive testing procedures. An effective screening method is needed for early treatment to improve the quality of the ASD patient’s life. Several conventional studies thrived on attaining ASD identification with machine learning and deep learning) but lacked in accuracy and computation. Therefore, the propounded system employs modified multi-layer perceptron (MLP) with cross-weighted attention mechanism. MLP is utilized for its capability in resolving intricate nonlinear complications, handling huge datasets, and enhancing the accuracy of the model. Though existing studies have utilized MLP for various identification processes, it still lacks identification speed, has overfitting issues, and requires more parameters. To overcome these issues, the proposed system employs cross-weighted attention mechanism, to enhance the identification process. As few researchers focused only on identifying and diagnosing the ASD adult, the proposed system uses autism screening in adult datasets and intends to improve the diagnosis of ASD adult patients. Attention ASD-modified MLP with cross-weighted algorithm is applied to classify and perform with various algorithms such as random forest, MLP, and Naïve Bayes. Furthermore, the performance is examined with certain metrics to calculate the efficacy of the proposed system.