[Retracted] Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model

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