Autism Spectrum Disorder detection by an Intelligent Deep Learning Network

Abstract

Mental disorder Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects a person’s behaviour and communication. In today’s world, ASD is gaining energy faster than ever before, limiting social and cognitive capacities while exhibiting varying expressions from one person to the next. Despite the fact that a lot of research has been done on ASD using various methodologies, the results haven’t shown enough progress in precision and execution. Similarly, finding mental imbalance characteristics through screening exams is time-consuming and costly. The main goal is to present a powerful half breed forecast model using high-level AI approaches like as SVM (Support Vector Machine) and NB (Nave Bayes). When compared to current models that use a real dataset acquired from people, the evaluation findings reveal that the suggested forecast model provides higher results in terms of precision, execution, and accuracy. Furthermore, if the problem is identified, space-specific professionals from designated urban regions are advised.