Detecting Autism by Analyzing a Simulated Social Interaction

• Published in: Machine Learning and Knowledge Discovery in Databases Vol. 11051; pp. 193 - 208
• Main Authors: Drimalla, Hanna, Landwehr, Niels, Baskow, Irina, Behnia, Behnoush, Roepke, Stefan, Dziobek, Isabel, Scheffer, Tobias
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 9783030109240; 3030109240
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-10925-7_12

Diagnosing autism spectrum conditions takes several hours by well-trained practitioners; therefore, standardized questionnaires are widely used for first-level screening. Questionnaires as a diagnostic tool, however, rely on self-reflection—which is typically impaired in individuals with autism spectrum condition. We develop an alternative screening mechanism in which subjects engage in a simulated social interaction. During this interaction, the subjects’ voice, eye gaze, and facial expression are tracked, and features are extracted that serve as input to a predictive model. We find that a random-forest classifier on these features can detect autism spectrum condition accurately and functionally independently of diagnostic questionnaires. We also find that a regression model estimates the severity of the condition more accurately than the reference screening method.