Table 9.

The performance evaluation results of machine and deep learning models on the third balanced dataset.

Model	Accuracy (%)	Precision (%)	Recall or Sensitivity (%)	Specificity (%)	F1-Score (%)	AUC (%)	MCC (%)
LR	85.00	84.62	91.67	75.00	88.00	85.20	68.47
KNN	70.00	65.38	85.00	55.00	73.91	72.00	41.93
DT	72.50	73.08	82.61	58.82	77.55	72.30	42.94
SVM	87.50	86.63	90.34	87.69	87.34	87.92	76.92
NB	85.00	88.46	88.46	78.57	88.46	83.50	67.03
ET	90.00	88.46	95.83	81.25	92.00	90.70	79.17
RF	85.00	80.77	95.45	72.22	87.50	86.80	70.59
XGBOOST	81.25	79.89	83.49	82.74	80.48	81.12	64.16
DNN	92.50	86.96	100	85.00	93.02	92.20	85.97
CNN	76.87	72.56	83.26	77.09	74.78	76.87	56.82
RNN	79.35	83.47	79.00	83.64	79.72	80.22	61.53
LSTM	51.25	100	50.59	25.00	67.05	51.12	5.18