Screening and classification of Autism Spectrum Disorder using Machine Learning

Abstract

The inability of the person to communicate with the others either verbally and non-verbally, also portraying certain difficulties in their social behavior or showing a repetitive behavior is a development disorder called as Autism Spectrum Disorder or commonly ASD. The assessment or screening of the ASD requires a countless things with a large number of reactions to be noted mostly conducted by clinical staff, guardian and some self assessment tools to identify it. Using the modern tools such as artificial intelligence, machine learning and computational intelligence a more potential methods can be designed to improve the efficiency and accuracy of the screening tools. Using these new technologies smaller platforms can be built to improve screening or having an option to help moving towards precise opinion. In this paper screening of disorder is built and also machine learning techniques are applied on the datasets related to the autism spectrum disorder. Autism Spectrum Disorder is a social behavioral disorder and the application of machine learning techniques helps in the better screening and diagnosis of the disease. This helps the medical practitioners, nursing staff or parents that deal with autism affected patients to better treat the patients, and also in the early detection of the disorder. The results obtained from the machine learning applications can be in turn used to develop more better and accurate screening tools. This helps in the ease of treatment.