Table 12.

Comparison of the model developed in this work with other related works.

Study	Data splitting	Participants	Features/representation	Classifier	ACC	Prec	Recall	AUC	Threshold	Kappa
3	Random samples, 2 s segments	3621	Spectrogram and log-melspectrogram from coughing sounds	ResNet18	NA	NA	0.9	0.72	Manipulated to yield 90% sensitivity	NA
9	Used the whole audio and chunked audio	2000	Hand-crafted and Vggish extracted features including tempo and MFCC from coughing and breath sounds	Logistic regression, gradient boosting trees, and SVM	NA	0.72	0.69	0.80	NA	NA
31	Split the sound files into 6 s audio splits	5320	Muscular degradation, vocal cords, sentiment, MFCC	Three pre-trained ResNet50	1	0.94	0.985	0.97	Manipulated	NA
Our method	Segment the coughing sounds into a single non-overlapping coughing sound	1502	Spectrogram, MelSpectrum, tonal, raw, MFCC, power spectrum, chroma	Ensemble of CNN classifiers	0.77	0.80	0.71	0.77	0.5	0.53

This comparison is not intended to be a head-to-head comparison because several implementation details are not available.