Detection of Autism Spectrum Disorder using Machine Learning: A Review

Abstract

Abstract: Social interaction and communication impairments are caused by an illness known as Autism Spectrum Disorder (ASD), which has neurological and genetic components. Statistics from the World Health Organization (WHO) show that the number of people with ASD diagnoses is progressively rising. The majority of recent research focuses on data gathering, brain image analysis, and clinical diagnosis; it does not address the diagnosis of ASD using machine learning. Currently, the only techniques available for diagnosing ASD are clinical standardized testing. This results in longer diagnostic times as well as a sharp rise in medical expenses. Machine learning approaches are being used to supplement traditional methods in order to enhance diagnosis precision and time required. The dataset is being subjected to several machine learning approaches, with the aim of constructing predictive models that are contingent on the results. The best accurate machine learning model to identify the disease in its early stages is then found by analyzing each technique according to its assessment metrics.