Autism spectrum disorder detection in toddlers for early diagnosis using machine learning

Abstract

Autism spectrum disorder (ASD) is a disorder where patients are unable to express and interact. Recently it is an issue to be concerned that one in 59 children has identified as an autism spectrum disorder patient. ASDs start from childhood but symptoms can be detected in adulthood. That is why these children are not being able to have proper treatment at an early age and that causes more complexity in their health. Research shows that a diagnosis of autism at an earlier age can be more reliable and stable. Therefore, our study aims to estimate ASD (autism spectrum disorder) at a sooner possible time and increase more accuracy than the previous research and reduce medical costs. In our thesis paper, we want to predict and distinguish between autistic and non-autistic children by using a machine learning approach. Firstly, we have gathered data from the surveillance side as much as possible. We also set some particular questions and try to find maximum accurate answers to all questions. Furthermore, supervised learning algorithms are applied to diagnosis whether children meet the symptoms for ASD. Among all applied algorithms KNN and Random Forest shows maximum accuracy and speed to diagnosis. Above all, our final goal is to create an online tool that can provide machine learning-based analysis to a user to detect autism at an early age precisely.