A Survey: Approaches for Detecting the Autism Spectrum Disorder

Abstract

A brain disease mean autism spectrum disorder affects a person's ability to connect, communicate, and remember. Though autism is capable of being diagnosed regardless of age, most of the disorder's signs begin to appear around its initial two years of life and increase as time goes on. People with autism suffer from a wide range of difficulties, such sensory problems, action impairments, intellectual disabilities, and psychological disorders including depression and anxiety. Autism has been rising at an unacceptably rapid pace surrounding around the globe. Autism detection involves an enormous amount of time and money. The early detection of autism might be highly advantageous in regards to treating patients with the right medical treatments at the correct moment in time. It could prevent the individual's illnesses before developing severe and could help in decreasing future expenses associated to a diagnosis that was delayed. Thereby, the requirement to develop a rapid, trustworthy, and simple examination device that can make predictions is essential. Autism Spectrum Disorder (ASD) has been gaining momentum presently more quickly than at any time earlier. Diagnostic evaluation of autistic characteristics is extremely expensive and time-consuming as well. The advancement of algorithms for machine learning (ML) and Artificial intelligence (AI) have made it achievable to identify autism fairly earlier. Although the reality of numerous studies have been carried out performed utilising different techniques, these studies have not contributed to any definitive conclusions regarding the capacity of predicting autism attributes in regards to different age categories. Thereby, the objective of this research is to predict Autism among people of all ages and to provide an effective model for prediction using various ML approaches.