Table 6.

Comparative analysis of the designed smart healthcare model with existing classifiers by taking the k-fold validation as 5.

Measures	DNN [32]	RNN [33]	LSTM [34]	CNN [35]	CCNN [26]	GSO-CCNN
“Accuracy”	0.8378	0.8671	0.897	0.8965	0.9218	0.9721
“Sensitivity”	0.91844	0.99837	0.89654	0.87961	0.93522	0.97667
“Specificity”	0.77182	0.76807	0.8975	0.91443	0.91037	0.96525
“Precision”	0.76707	0.76456	0.90454	0.91608	0.89887	0.97683
“FPR”	0.22818	0.23193	0.1025	0.085567	0.08963	0.03475
“FNR”	0.081556	0.001628	0.10346	0.12039	0.064783	0.023333
“NPV”	0.77182	0.76807	0.8975	0.91443	0.91037	0.96525
“FDR”	0.23293	0.23544	0.09546	0.083923	0.10113	0.023171
“F1-score”	0.83596	0.86596	0.90052	0.89747	0.91668	0.97675
“MCC”	0.68888	0.7647	0.79378	0.79374	0.84365	0.94188