Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations

Table 4

Top 10 models developed on dataset 2.

	Settings	Feature set	Accuracy	Sensitivity	Specificity	Precision	F-score	AUC
SVM	RBF default	4	87.83	83.4	90.3	82.9	0.832	0.942
C5	Boosting	3	87.44	81.8	90.6	82.7	0.822	0.94
SVM	RBF default	3	87.59	82.7	90.3	82.4	0.826	0.938
C5	Boosting	4	87.88	79.9	92.4	85.5	0.826	0.938
RF	Default	4	87.86	85.7	89.1	81.5	0.836	0.931
C5	Boosting	2	86.68	78.5	91.5	84.3	0.813	0.927
C5	Boosting	1	85.99	77.2	90.8	82.2	0.797	0.926
SVM	RBF default	2	86.61	79	91.1	83.7	0.813	0.926
MLP	1.10	3	85.38	77	90	80.9	0.789	0.923
RF	Default	1	85.26	85.2	85.3	76.2	0.804	0.923

For MLPs, the numbers for MLP indicate the number of layers, the number of neurons in hidden layer 1, and the number of neurons in hidden layer 2.