Machine Learning Hybrid Model for the Prediction of Chronic Kidney Disease

Table 2

Equations for accuracy measurement.

S. no	Authors	Accuracy equations
1	Padmanaban and Parthiban [8]	Precision i = TPi/TPi + FPi
2	Charleonnan et al. [9]	ACC = (TP + TN)/(P + N)
3	Ghosh et al. [7]	The results of performance degree indices are dependent on TP, TN, FP, and FN
4	Fu et al. [10]	Ext. values = points > Q3 + 1.5 (IQR) points < Q1 − 1.5 (IQR)
5	Devika et al. [11]	Accuracy = number of properly classified samples/total variety of samples
6	Revathy et al. [12]	Accuracy  Accuracy = TP + TN/TP + TN + FP + FN
7	Nishat et al. [14]	Accuracy  Accuracy = TP + TN/TP + TN + FP + FN
8	Rabby et al. [13]	Descriptive analysis of the data as well as the experimental results
9	Pouriyeh et al. [15]	Finding most significant feature using chi-square test
10	Jabbar et al. [16]	Experimental results only

True positive (TP) = list contains stated cases that are correctly categorized with CKD. False positive (FP) = list contains set that is inaccurately categorized with CKD. True negative (TN) = list contains stated instances that are correctly categorized with CKD. False negative (FN) = list contains set of instances that are exactly categorized with CKD.