Table 3.

The best machine learning models

Dataset	Model	AUC ± SD CI	ACC ± SD CI	SEN ± SD CI	SPE ± SD CI
ED	MRMR_XGB	0.73 ± 0.096 0.72–0.73	0.73 ± 0.094 0.72–0.73	0.73 ± 0.140 0.72–0.73	0.73 ± 0.140 0.72–0.74
ES	ANOVA_MLP	0.69 ± 0.091 0.69–0.70	0.69 ± 0.092 0.69–0.70	0.83 ± 0.110 0.82–0.83	0.56 ± 0.150 0.55–0.57
ES	RFE_KNN	0.68 ± 0.092 0.68–0.69	0.69 ± 0.094 0.68–0.69	0.55 ± 0.150 0.54–0.56	0.81 ± 0.120 0.81–0.82
ED&ES	RFE_KNN	0.65 ± 0.092 0.64–0.65	0.65 ± 0.088 0.64–0.65	0.65 ± 0.140 0.64–0.66	0.64 ± 0.150 0.63–0.65
ES	MRMR_MLP	0.65 ± 0.093 0.64–0.66	0.65 ± 0.090 0.64–0.66	0.65 ± 0.150 0.64–0.66	0.65 ± 0.140 0.64–0.66

SD standard deviation, CI confidence interval, ACC accuracy, SPE specificity, SEN sensitivity