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Combining Gaze and Demographic Feature Desciptors for Autism Classification

Citation Author(s):
Shaun Canavan, Melanie Chen, Song Chen, Robert Valdez, Miles Yaeger, Huiyi Lin, Lijun Yin
Submitted by:
Shaun Canavan
Last updated:
19 September 2017 - 8:23pm
Document Type:
Poster
Document Year:
2017
Event:
Presenters:
Shaun Canavan
Paper Code:
WA-PA.7
 

People with autism suffer from social challenges and communication difficulties, which may prevent them from leading a fruitful and enjoyable life. It is imperative to diagnose and start treatments for autism as early as possible and, in order to do so, accurate methods of identifying the disorder are vital. We propose a novel method for classifying autism through the use of eye gaze and demographic feature descriptors that include a subject’s age and gender. We construct feature descriptors that incorporate the subject’s age and gender, as well as features based on eye gaze data. Using eye gaze information from the National Database for Autism Research, we tested our constructed feature descriptors on three different classifiers; random regression forests, C4.5 decision tree, and PART. Our proposed method for classifying autism resulted in a top classification rate of 96.2%.

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