Table 4.

Comparative analysis of the designed smart healthcare model with existing classifiers.

Measures	DNN [32]	RNN [33]	LSTM [34]	CNN [35]	CCNN [26]	GSO-CCNN
“Accuracy”	0.6767	0.7558	0.8098	0.9085	0.9158	0.9499
“Sensitivity”	0.97889	0.88907	0.98654	0.94621	0.905	0.93483
“Specificity”	0.42945	0.65526	0.61833	0.86845	0.925	0.9725
“Precision”	0.58399	0.6605	0.73686	0.88423	0.91134	0.98077
“FPR”	0.57055	0.34474	0.38167	0.13155	0.075	0.0275
“FNR”	0.021111	0.11093	0.013462	0.053786	0.095	0.065167
“NPV”	0.42945	0.65526	0.61833	0.86845	0.925	0.9725
“FDR”	0.41601	0.3395	0.26314	0.11577	0.08866	0.019234
“F1-score”	0.73155	0.75793	0.84361	0.91417	0.90816	0.95725
“MCC”	0.47189	0.54579	0.65709	0.81859	0.83045	0.89834