Predicting Autism Spectrum Disorder Using Various Machine Learning Techniques

Abstract

A neurological disorder referred to as ASD autism spectrum disorder exists that may slow down speaking, linguistic abilities interpersonal skills. The symptoms typically occur within the initial period. Although ASD is mostly brought on by hereditary and external factors, it can occur from the time after newborn. Through the development phase, it can impact roughly one in every hundred people worldwide. Early detection and treatment can result in improvement; unfortunately, most kids with ASD do not acquire an accurate diagnosis and usually miss the opportunity for treatment; guardians may be hesitant to accept their child’s psychological progression which differs from one’s own physical growth. The delay in diagnosis prevents a toddler from receiving the necessary support and care to reach their full potential; at present, clinical standardised tests are the only available methods for diagnosing ASD. But they are time consuming, and costly efforts are underway to improve traditional procedures. Researchers have used approaches like support vector machines (SVM) and random forest classification methods (RFC) to build predictive model in order to enhance efficiency and accuracy. The study’s aim is to identify a child's vulnerability to ASD within the early phases aiding with early diagnosis. The study employed a systematic methodology to assess patient data over the previous 10 years who had disorder and non-chemical abnormalities. Findings demonstrate the effectiveness of using SVM and RFC, the RFC achieving 100% accuracies for all datasets. Early detection of ASD is crucial as larger amounts of information and testing can result in an increased accuracy for AI-assisted autism spectrum disorder which performs the exceptional among three arrangements for ordering ASD details.