Data Mining Technique for Diagnosing Autism Spectrum Disorder

Abstract

Early detection of autistic symptoms can help lower overall medical expenses, which is beneficial given that autism is a developmental disease that is associated with high medical costs. To assess whether or not a kid may have autism spectrum disorder (ASD), screening for ASD involves asking the child's parents, caregivers, and other members of the child's immediate family a series of questions. The current methods for screening for autism, such as the autistic quotient (AQ), might require a significant number of questions in addition to careful question design, which can make an autism examination more time-consuming. The effectiveness and reliability of the test could be improved, for example, by employing data mining strategies. It could be possible to create a system that can foretell ASD at an early stage and give patients, caregivers, and medical professionals dependable and precise findings on the probable need for expert diagnostic services. This research aims to develop a reliable model for estimating the likelihood of an individual being diagnosed with autism spectrum disorder between the ages of 4 and 17. To identify varying degrees of autism, one such model was constructed by utilizing the stochastic gradient descent (SGD) algorithm. Mining data is typically understood to be a decision-making process that enables more effective utilization of available resources in terms of overall performance. The results showed that the suggested prediction model, which used the stochastic gradient descent (SGD) algorithm, could find ASD with an average error of 0.03% and an accuracy of up to 94.5%.