Abstract
Autism is one of those psychiatric disorders that affect an individual's social, personal, and professional sphere(s). Autism, especially in children, is one of the most common behavioral disorders, wherein lack of good communication understandability exists throughout adulthood. The prescribed treatment of an autistic child depends completely upon an exhaustive, accurate examination of the child, which is very difficult for clinicians and consultants. Since autism is a complicated and very difficult psychiatric disorder, clinicians have joined hands with computational biologists to solve the foundations underlying the detection and diagnosis of autism. There is enormous literature evidence that highlights the pros of using machine learning techniques to develop an efficient, accurate, and robust autism detection and diagnosis system. Based on soft computing approaches, many researchers have proposed fuzzy logic-based solutions for modeling and predicting autism spectrum disorder. This chapter highlights the current scenario of autism detection and diagnosis, and how a soft computing-based intelligent agent—fuzzy logic systems—is being used to predict autism and its grades in children.
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