An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application

Table 11

Experimental result (accuracy (%)) comparison for CKD-15 with state-of-the-art methodologies.

Methodology	NB	SVM	LR	KNN	PAC	RF	DT	GB	SGD	ADAB	MLP
Nishat et al. [3]	90.50	99.36	99.36	94.3	—	—	98.10	—	—	—	94.3
Gupta et al. [57]	—	—	99.24	—	—	94.16	98.48	—
Herath et al. [1]	93.33	—	95.00	98.33	—	100	100	—	—	100	—
Chu et al. [58]	95	—	96	—	—	99	98	—	98	96	98
Gokiladevi et al. [59]	—	73.75	94.68	67.5	—	98.75	96.25	—	—	—	—
Chittora et al. [19]	—	94.63	71.71	64.39	—	98.75	96.25	—	—	—	—
Islam et al. [30]	88.33	96.66	—	59.00	—	97.50	97.50	97.50	97.50	98.30	—
Dritsas et al. [60]	98.40	—	97.40	98.40	—	98.90	97.40	—	—	—	—
Kaur et al. [61]	—	—	—	74.00	—	96.00	95.00	—	—	—	—
Proposed model without PCA	94.04	62.91	95.36	70.2	54.3	100	100	99.34	73.51	100	92.72
Proposed model	96.03	100	100	100	100	100	100	100	100	100	100

The best results are indicated in bold.