Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

Table 2

Comparison of random forest (RF), support vector machine (SVM), multichannel deep neural network (DNN), and multichannel deep attention neural network (DANN) classifiers trained using 10-fold cross validation on the entire dataset.

Method	Accuracy	Sensitivity	Precision	F-Score	Specificity
RF	0.659 ± 0.018	0.689 ± 0.106	0.656 ± 0.012	0.671 ± 0.023	0.628 ± 0.081
SVM	0.693 ± 0.059	0.713 ± 0.059	0.696 ± 0.072	0.702 ± 0.048	0.673 ± 0.113
Multichannel DNN	0.707 ± 0.027	0.673 ± 0.088	0.740 ± 0.106	0.718 ± 0.060	0.700 ± 0.067
Multichannel DANN	0.732 ± 0.024	0.745 ± 0.115	0.730 ± 0.053	0.736 ± 0.042	0.717 ± 0.101

All data are mean and standard deviation. The highest metrics were marked as bold.