Early Detection of Autism Spectrum Disorder Using Machine Learning Classification Techniques (EDASD-MLCT)

Abstract

Autism spectrum disorder (ASD) is a neuro-developmental condition that impairs a person's capacity for speaking, making announcements, and learning new things. Autism can be examined at each level of the age spectrum. Autism sufferers utilise meditation to examine difficult topics, learning disabilities, and other concerns. Autism-related difficulties might affect a person's movement, sense of smell, and mental health. Controlling ASDs requires early discovery of them. The ASD prediction framework supports the behavioural aspect-based analytic model in this experiment without the need for any equipment. The main focus of the ASD prediction approach is the system's utilised child and adolescent analytic model. The behavioural parameters are gathered using the Autism Query collections.