Machine Learning based Autism Grading for Clinical Decision Making

Abstract

Autism spectrum disorder is a pervasive developmental disorder that affects the behavioral and communication function of the children. It shows poor performance in communication, social and cognitive abilities, which are generally characterized by developmental delays and abnormal activities in their regular work. Early intervention can reduce the autism spectrum disorders. Machine learning techniques are used to detect autistic features in childhood. The prediction models are implemented as classification problem in which model is constructed by using real-time autism dataset. The proposed work is use Backpropagation and learning vector quantization with different distance measures like Euclidean Distance, Manhattan Distance, and City Block Distance to predict whether a child has autism spectrum disorder and also defines the grade of the autism. So that it can be supported for the clinical decision making. It enables automated clinical autism spectrum disorder diagnostic process using machine learning models.