ABSTRACT
Autism Spectrum Disorder (Autism) is a developmental disorder that impedes the social and communication capabilities of a person through out his life. Early detection of autism is critical in contributing to better prognosis. In this study, the use of home videos to provide accessible diagnosis is investigated. A machine learning approach is adopted to detect autism from home videos. Feature selection and state-of-the-art classification methods are applied to provide a sound diagnosis based on home video ratings obtained from non-clinicians feedback. Our models results indicate that home videos can effectively detect autistic group with True Positive Rate reaching 94.05% using Support Vector Machines and backwards feature selection. In this study, human-interpretable models are presented to elucidate the reasoning behind the classification process and its subsequent decision. In addition, the prime features that need to be monitored for early autism detection are revealed.
