Table 1.

Performance of the classifiers with respect to accuracy, sensitivity, specificity, F1-score, and false discovery rate on test sets.^a

Model	Accuracy	Sensitivity	Specificity	F1-score	False discovery rate
DeepAutism	0.886	0.881	0.893	0.905	0.072
Naive Bayes	0.679	0.706	0.633	0.733	0.237
Random forest	0.808	0.785	0.857	0.848	0.079
Logistic regression	0.704	0.715	0.683	0.761	0.186
Support vector machine	0.789	0.773	0.821	0.831	0.101
Deep neural network	0.804	0.766	0.885	0.842	0.073

^aItalicized data demonstrate the best performance; DeepAutism outperformed other models on all the metrics.