An Approach Towards Autism Spectrum Disorder Detection: A Review

Abstract

The autism spectrum disorder (ASD) has been prolife rating rapidly around the world. Autism Spectrum Disorder (ASD) is a neurological disorder which might have a lifelong impact on the language learning, speech, cognitive, and social skills of an individual. Its symptoms usually show up in the developmental stages. ASD is mainly caused by genetics or by environmental factors, however, its conditions can be improved by detecting and treating it at earlier stages. In many aspects of research, machine learning played an extensive role for development especially in terms of data analytics. Implement multiple machine learning techniques, to predict Autism Spectrum Disorder (ASD) over large scale datasets. Autism is also identified as a range of conditions categorized by various challenges such as social skills, repetitive behaviors, Speech and non-verbal communication and unique strengths and differences. The ultimate goal is to train the data model with the set of training data and then testing and evaluating the data model using the test data. As a result, the research will come up with a solution that applies machine learning to detect autism.