Study on Early Detection of Autism Using Genetic and Kinematic Biomarker

Abstract

Over these years prevalence of autism has shown a drastic increase. A great deal of progress has been made in recognizing autism but still a lot to do. The impact of autism on the individual differ due to difference in the degree of severity and the wide range of symptoms. Early detection can in turn ameliorate overall development of child and help to shorten the diagnostic odyssey that many families experience. One of the great things about machine learning is the capability of computers to work on the actual data to find the complex relationships through different algorithms which in turn result in accurate predictions. Thus by examining the various risk factors that contribute to autism, machine learning approach seems to be beneficial for identifying the potential markers for autism. This study covers machine learning approaches that are used for identification of autism which facilitate early detection and better diagnosis of autistic individuals using different biomarkers.