Abstract
Adult referrals to specialist autism spectrum disorder diagnostic services have increased in recent years, placing strain on existing services and illustrating the need for the development of a reliable screening tool, in order to identify and prioritize patients most likely to receive an ASD diagnosis. In this work a detailed overview of existing approaches is presented and a data driven analysis using machine learning is applied on a dataset of adult autism cases consisting of 192 cases. Our results show initial promise, achieving total positive rate (i.e., correctly classified instances to all instances ratio) up to 88.5%, but also point to limitations of currently available data, opening up avenues for further research. The main direction of this research is the development of a novel autism screening tool for adults (ASTA) also introduced in this work and preliminary results indicate the ASTA is suitable for use as a screening tool for adult populations in clinical settings.
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