Table 1.

Performance comparison of iBCE-EL with other ML-based methods on the benchmarking data set.

Method	Matthews correlation coefficient (MCC)	Accuracy	Sensitivity	Specificity	AUC	P-value
iBCE-EL	0.454	0.729	0.716	0.739	0.782	–
GB	0.446	0.725	0.712	0.735	0.773	0.051
ERT	0.437	0.718	0.734	0.705	0.776	0.205
RF	0.434	0.718	0.717	0.719	0.770	0.051
AB	0.396	0.702	0.662	0.722	0.737	1.2E−16
k-NN	0.301	0.644	0.715	0.591	0.691	1.1E−9
SVM	0.295	0.634	0.634	0.602	0.696	<2.2E−16
LBtope	0.330	0.667	0.660	0.672	0.730	–

The first column represents the methods developed in this study. The columns 2–6 respectively represent the MCC, accuracy, sensitivity, specificity, and AUC value. The last column represents McNemar’s Chi-squared test was used to evaluate the performance between iBCE-EL and other methods. A P value <0.05 was considered to indicate a statistically significant difference between iBCE-EL and the selected method (shown in bold). For comparison, we have also included LBtope (LBtope_variable_nr) cross-validation performance on non-redundant data set.