Combining gaze and demographic feature descriptors for autism classification

Shaun J. Canavan, Melanie Chen, Song Chen, Robert Valdez, Miles Yaeger, H. Lin, L. Yin
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引用次数: 12

Abstract

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%.
结合凝视与人口统计学特征描述符进行自闭症分类
自闭症患者遭受社会挑战和沟通困难,这可能会妨碍他们过上富有成效和愉快的生活。尽早诊断并开始治疗自闭症是非常必要的,为了做到这一点,准确识别这种疾病的方法至关重要。我们提出了一种新的方法来分类自闭症通过使用眼睛凝视和人口特征描述符,包括受试者的年龄和性别。我们构建了包含受试者年龄和性别的特征描述符,以及基于眼睛注视数据的特征。使用来自国家自闭症研究数据库的眼睛凝视信息,我们在三种不同的分类器上测试了我们构建的特征描述符;随机回归森林、C4.5决策树和PART。我们提出的自闭症分类方法的最高分类率为96.2%。
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