Table 3 Evaluation metrics table to classify breathing sounds of 23 patients with a tracheostomy tube: normal and abnormal sounds.

	Input	Name of the classifier	Accuracy	Sensitivity	Specificity	Positive predictive value	Negative predictive value	Area under curve
CNN	Spectrogram	AlexNet	0.9140	0.9401	0.8690	0.9253	0.8936	0.9586
		VGG_16	0.9090	0.9341	0.8655	0.9231	0.8838	0.9527
		ResNet_50	0.9330	0.9381	0.9241	0.9553	0.8963	0.9650
		Inception_v3	0.9317	0.9261	0.9414	0.9647	0.8806	0.9608
		MobileNet	0.9241	0.9441	0.8897	0.9366	0.9021	0.9549
SVM	MFCC(20)	3rd polynomial	0.8306	0.8922	0.7241	0.8482	0.7955	0.9248
	MFCC(20)	RBF	0.9153	0.9142	0.9172	0.9502	0.8608	0.9681
	MFCC(40)	3rd polynomial	0.8609	0.8703	0.8448	0.9064	0.7903	0.9257
	MFCC(40)	RBF	0.9102	0.9142	0.9034	0.9424	0.8590	0.9598
kNN	MFCC(20)	k = 3	0.8900	0.9142	0.8483	0.9124	0.8512	0.9098
		k = 5	0.8786	0.8882	0.8621	0.9175	0.8170	0.9159
		k = 7	0.8824	0.8842	0.8793	0.9268	0.8147	0.9369
	MFCC(40)	k = 3	0.9102	0.9341	0.8690	0.9249	0.8842	0.9102
		k = 5	0.8976	0.9102	0.8759	0.9268	0.8495	0.9249
		k = 7	0.8913	0.9042	0.8690	0.9226	0.8400	0.9406

CNN convolution neural network; SVM support vector machine; kNN k-nearest neighbor; RBF radial bias function.

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