Table 6.

Evaluation parameters.

S. no.	Classification algorithm	Sensitivity	Specificity	AUC	Accuracy
1	K-nearest neighbour	0.786	0.659	0.838	78.58
2	Random forest	0.798	0.714	0.836	79.83
3	Decision trees	0.761	0.691	0.785	76.07
4	Multilayer perceptron	0.776	0.679	0.846	77.60

Bold means the improvement in sensitivity, specificity, AUC, and accuracy after the proposed method.