A New Computational Intelligence Approach To Understand Autism Spectrum Disorders

Autism Spectrum Disorder is a neurodevelopmental disorder that is the fastest growing disability in the world. The first point of contact to identify this type of disorder is the clinical practitioners, therapist or sometimes it is observed in themselves or their family members. Therefore, an early diagnosis and treatment strategy for ASD is highly necessary. To that end, we seek a machine intelligence approach that takes into account function-to-class correlations instead of function-to-fact. This research aims to classify features that are less relevant in popular ASD assessments, which have an effect on ASD in current ASD tools. The use of intelligent approaches focused on machine learning to minimize screening time for ASD has been explored in behavioral science, increasing sensitivity, precision, and accuracy. In order to accomplish one or more of the aforementioned objectives, these models are to be used in a screening tool.