Table 13.

Performance analysis of different optimization techniques on PTBDB dataset.

Optimization	Model	Accuracy	Precision	TPR	TNR	FPR	FNR	F1-Score	AUC	MCC	Cohen’s Kappa	MAE	MSE	RMSE
Adagrad	CNN	80.28	83.76	75.11	85.44	14.56	24.89	79.20	86.02	60.88	60.55	31.12	14.93	38.64
	DNN	78.44	83.66	70.68	86.20	13.80	29.32	76.63	85.93	57.58	56.89	31.37	15.06	38.81
Adam	CNN	99.43	99.57	99.29	99.57	0.43	0.71	99.43	99.98	98.86	98.86	0.92	0.43	6.56
	DNN	96.17	96.63	95.67	96.67	3.33	4.33	96.15	99.27	92.34	92.34	6.03	2.96	17.19
Adamax	CNN	98.22	98.70	97.72	98.72	1.28	2.28	98.21	99.77	96.44	96.43	4.37	1.54	12.40
	DNN	95.74	96.79	94.62	96.86	3.14	5.38	95.69	99.10	91.51	91.48	7.08	3.28	18.12
RMSprop	CNN	98.95	99.38	98.52	99.38	0.62	1.48	98.95	99.89	97.91	97.91	1.71	0.82	9.06
	DNN	96.07	96.90	95.19	96.96	3.04	4.81	96.04	99.21	92.16	92.15	6.43	3.00	17.31