Early prediction of chronic kidney disease based on ensemble of deep learning models and optimizers

Journal of Electrical Systems and Information Technology

Table 9 Comparison of performance metrics for 6-month data obtained from the proposed models and the literature

Model		Precision	Sensitivity	F1-score	Accuracy
Individual classifiers in Ensemble model	CNN_Adamax	0.95	0.95	0.95	0.95
	LSTM_Adam	0.96	0.96	0.96	0.96
	LSTM-BLSTM_Adamax	0.97	0.97	0.97	0.97
Ensemble model		0.98	0.98	0.98	0.98
Results for the previous work [9]	LightGBM [9]	0.426	0.685	0.525	0.751
	Logistic [9]	0.405	0.664	0.503	0.736
	Random forest [9]	0.390	0.652	0.488	0.725
	Decision tree [9]	0.395	0.622	0.483	0.732

The bold, underlined values represent the best optimizer's performance for each model