A Data-Driven Approach for Detecting Autism Spectrum Disorders

Autism spectrum disorders (ASDs) are a group of conditions characterized by impairments in reciprocal social interaction and by the presence of restricted and repetitive behaviors. Current ASD detection mechanisms are either subjective (survey-based) or focus only on responses to a single stimulus. In this work, we develop machine learning methods for predicting ASD based on electrocardiogram (ECG) and skin conductance (SC) data collected during a sensory challenge protocol (SCP) in which the reactions to eight stimuli were observed from 25 children with ASD and 25 typically developing children between 5 and 12 years of age. The length of the time series makes it difficult to utilize traditional machine learning algorithms to analyze these types of data. Instead, we developed feature processing techniques which allow efficient analysis of the series without loss of effectiveness. The results of our analysis of the protocol time series confirmed our hypothesis that autistic children are greatly affected by certain sensory stimulation. Moreover, our ensemble ASD prediction model achieved 93.33% accuracy, which is 13.33% higher than the best of 8 different baseline models we tested.