Classification of autistic and normal children using analysis of eye-tracking data from computer games

With the increasingly growing incidence of autism disorder, the early diagnosis of this disorder is of utmost importance. The appropriate diagnosis of this condition depends on the correct and accurate decision of the specialist. Thus, given the limited number of specialists in this area, the development of an efficient classification system is necessary. This study aims to analyze the eye-tracking data obtained from computer games to classify autistic and normal children. Twenty normal children from Namjoo public elementary school and 20 autistic children from the Daily Rehabilitation Clinic for Children with Special Disorders from the Tehran metropolis were selected for this study. Data were analyzed using a multilayer perceptron neural network with a backpropagation learning algorithm and a scaled conjugate gradient training algorithm. The classification results were evaluated using the confusion matrix. The graphs from the game and the children's gaze fixation data were analyzed in this study to achieve a person-to-person evaluation and extract suitable features to classify autistic and typical children. The highest output precision and accuracy were obtained as 76.6% and 73.8%, respectively, for the case with all features considered.