Autism spectrum disorder (ASD) classification with three types of correlations based on ABIDE Ⅰ data

Abstract

Autism spectrum disorder (ASD) is a type of mental health disorder, and its prevalence worldwide is estimated at about one in 100 children. Accurate diagnosis of ASD as early as possible is very important for the treatment of patients in clinical applications. ABIDE Ⅰ dataset as a repository of ASD is used much for developing classifiers for ASD from typical controls. In this paper, we mainly consider three types of correlations including Pearson correlation, partial correlation, and tangent correlation together based on different numbers of regions of interest (ROIs) from only one atlas, and then twelve deep neural network models are used to train 884 subjects with 5, 10, 15, 20-fold cross-validation on two types of split methods including stratified and non-stratified methods. We first consider six metrics to compare the model performance among the split methods. The six metrics are F1-Score, precision, recall, accuracy, and specificity, area under the precision-recall curve (PRAUC), and area under the Receiver Characteristic Operator curve (ROCAUC). The study achieved the highest accuracy rate of 71.94% for 5-fold cross-validation, 72.64% for 10-fold cross-validation, 72.96% for 15-fold cross-validation, and 73.43% for 20-fold cross-validation.