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

Table 5

Top 10 models developed on dataset 3.

	Settings	Feature set	Accuracy	Sensitivity	Specificity	Precision	F-score	AUC
C5	Boosting	4	92.77	95.1	90.5	90.8	0.929	0.972
C5	Boosting	3	91.74	93.6	89.8	90.5	0.92	0.965
C5	Boosting	2	91.18	94.2	88	89.1	0.916	0.96
SVM	RBF default	4	90.16	92.7	87.7	88.1	0.903	0.956
C5	Boosting	1	89.28	91.3	87.3	87.7	0.895	0.952
SVM	RBF default	3	88.81	90.5	87.1	87.9	0.892	0.944
MLP	2.15.15 boosting	3	88.59	90.2	86.9	87.7	0.889	0.94
MLP	2.12.12 boosting	4	87.61	88.5	86.8	86.8	0.876	0.938
C5	Default	3	87.4	89.8	85	86.1	0.879	0.934
SVM	RBF default	2	86.34	86.6	86.1	86.6	0.866	0.932

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.