Automated identification of postural control for children with autism spectrum disorder using a machine learning approach.

It is unclear whether postural sway characteristics could be used as diagnostic biomarkers for autism spectrum disorder (ASD). The purpose of this study was to develop and validate an automated identification of postural control patterns in children with ASD using a machine learning approach. 50 children aged 5-12 years old were recruited and assigned into two groups: ASD (n = 25) and typically developing groups (n = 25). Participants were instructed to stand barefoot on two feet and maintain a stationary stance for 20 s during two conditions: (1) eyes open and (2) eyes closed. The center of pressure (COP) data were collected using a force plate. COP variables were computed, including linear displacement, total distance, sway area, and complexity. Six supervised machine learning classifiers were trained to classify the ASD postural control based on these COP variables. All machine learning classifiers successfully identified ASD postural control patterns based on the COP features with high accuracy rates (>0.800). The naïve Bayes method was the optimal means to identify ASD postural control with the highest accuracy rate (0.900), specificity (1.000), precision (1.000), F1 score (0.898) and satisfactory sensitivity (0.826). By increasing the sample size and analyzing more data/features of postural control, a better classification performance would be expected. The use of computer-aided machine learning to assess COP data is efficient, accurate, with minimum human intervention and thus, could benefit the diagnosis of ASD.