TABLE 3.

Proposed classification results with 10‐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	99.1	0.9	99.13	99.00	10.56
ESD	97.1	2.9	97.14	96.99	85.53
QSVM	97.3	2.7	97.38	97.03	44.73
LSVM	96.9	3.1	97.00	96.70	41.89
ESKNN	93.3	6.7	93.38	93.10	260.7
CSVM	96.2	3.8	96.29	95.99	50.25
MGSVM	94.8	5.2	94.95	94.20	51.41
CKNN	93.4	6.6	94.00	93.01	22.69
CGSVM	88.8	11.2	89.00	88.30	53.32
EBT	87.7	12.3	88.02	87.30	389.2