Table 3.

Evaluation results of the prediction models in the National Health and Nutrition Examination Survey dataset.

Feature Selection	Classifier	Accuracy	Sensitivity	Specificity	Precision	F-Score
SVM-RFE	LR	0.7349	0.6969	0.8874	0.7086	0.7027
	RF	0.8522	0.7904	0.8805	0.8157	0.8029
	KNN	0.8118	0.7432	0.8608	0.8105	0.7754
	MLP	0.8002	0.7171	0.8759	0.6816	0.6989
	NN	0.8339	0.7659	0.8397	0.7609	0.7634
	XGBoost	0.8248	0.7707	0.8512	0.8066	0.7882
RFFS	LR	0.8356	0.7169	0.8685	0.6938	0.7052
	RF	0.8741	0.7863	0.9065	0.7356	0.7601
	KNN	0.8444	0.7716	0.8635	0.7594	0.7655
	MLP	0.8221	0.7043	0.8949	0.6842	0.6941
	NN	0.8639	0.7651	0.9003	0.7534	0.7592
	XGBoost	0.9029	0.8507	0.9379	0.8264	0.8384
HFS	LR	0.7903	0.7781	0.8990	0.7732	0.7756
	RF	0.8961	0.8157	0.9136	0.7857	0.8004
	KNN	0.8363	0.7928	0.8990	0.7981	0.7954
	MLP	0.7918	0.7586	0.9083	0.7635	0.7610
	NN	0.8553	0.8173	0.8808	0.7934	0.8052
	XGBoost	0.9309	0.8944	0.9522	0.8874	0.8909

Highest scores are marked in bold.