TABLE 2.

Proposed classification results with 5‐fold validation strategy on dataset 1 using different machine learning algorithms, FNR is representing false negative rate

Classifier	Accuracy (%)	FNR (%)	Precision (%)	Sensitivity (%)	Time (s)
LD	99.0	1.0	99.05	98.97	08.63
ESD	97.1	2.9	97.13	97.05	69.58
QSVM	96.9	3.1	96.92	96.02	28.98
LSVM	96.0	4.0	96.03	95.93	27.56
ESKNN	92.8	7.2	92.84	92.00	263.5
CSVM	95.8	4.2	95.90	95.04	33.29
MGSVM	93.9	6.1	94.00	93.02	33.91
CKNN	92.0	8.0	92.09	91.95	19.60
CGSVM	88.4	11.6	88.50	82.95	34.35
EBT	86.5	13.5	86.64	86.30	238.2