A computational intelligent analysis of autism spectrum disorder using machine learning techniques

Abstract

Children between the ages of 12 and 24 months who have autism spectrum disorder (ASD) experience abnormalities in the brain that result in undesirable symptoms. Children with ASD struggle to comprehend what others are trying to say and or feel, and they experience extreme anxiety in social situations. Additionally, they have a hard time making friends and even living independently. The defective genes, which control the brain and govern how brain cells communicate with one another, are the primary cause of ASD because they alter brain function. Our primary goal is to assist therapists and parents of children with ASD in using current technologies, such as human intelligence and artificial intelligence, to treat ASD and assist those youngsters in obtaining better social interaction and societal integration. For the purpose of doing an early analysis of ASD, the data is divided into the following three categories: age, gender, and jaundice symptoms. The performance of machine learning algorithms can be influenced by a variety of factors, such as the size of the dataset and quality of the dataset, the choice of features, and the tuning of hyper-parameters. In this work, the support vector machine (SVM) yields 96% as the highest classification accuracy.