Table 6.

Performance comparison of different machine learning classifiers with the stacking classifier with values rounded off to the nearest two decimal positions

Classifier	Accuracy	Precision	Recall	F1-score	AUC
Logistic regression	98.13	98.83	98.60	98.71	97.73
Support vector classifier	98.13	99.29	98.13	98.71	98.12
Nu- Support vector classifier	97.44	97.47	99.07	98.26	96.07
K-Nearest classifier	98.13	99.29	98.13	98.71	98.12
MLP classifier	97.10	98.81	97.20	98.00	97.03
Gaussian naïve bayes	95.91	96.12	98.36	97.23	93.84
Bernoulli NB	94.89	98.77	94.16	96.41	95.50
Gradient boosting classifier	94.72	97.37	95.33	96.34	94.20
XGB classifier	96.59	99.28	96.03	97.62	97.07
Decision Tree classifier	94.72	97.37	95.33	96.34	94.20
Random forest classifier	96.08	96.13	98.60	97.35	93.95
Extra Trees classifier	96.76	97.01	98.60	98.80	95.21
Bagging classifier	98.13	98.60	98.83	98.72	97.53
AdaBoost classifier	95.06	97.84	95.33	96.57	94.83
LGB classifier	97.10	98.58	97.43	98.00	96.83
CatBoost classifier	97.96	99.29	97.90	98.59	98.00
HistGradient boosting classifier	96.08	99.27	95.33	97.26	96.72
Proposed method	98.30	99.29	98.36	98.83	98.24