Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations
Table 7
External validation on dataset 3.
Models
Settings
Feature set
Accuracy
Sensitivity
Specificity
Precision
F-score
AUC
C5
Boosting
1
92.56
0.955
0.919
0.720
0.821
0.974
C5
Boosting
2
91.81
0.964
0.908
0.695
0.808
0.98
SVM
RBF default
3
91.00
0.848
0.924
0.706
0.771
0.955
Ensemble 2
—
2
87.77
0.861
0.881
0.611
0.715
0.954
SVM
RBF default
2
88.24
0.890
0.881
0.618
0.729
0.953
Ensemble 1
—
1
88.75
0.819
0.902
0.645
0.722
0.949
C5
Boosting
3
86.51
0.935
0.850
0.575
0.712
0.948
Ensemble 3
—
3
88.18
0.783
0.903
0.637
0.702
0.931
MLP
2.15.15 boosting
3
87.95
0.767
0.904
0.634
0.694
0.914
MLP
2.12.12 boosting
4
87.31
0.754
0.899
0.618
0.679
0.914
Ensemble 4
—
4
86.62
0.770
0.887
0.596
0.672
0.91
C5
Boosting
4
85.64
0.748
0.880
0.575
0.650
0.889
C5
Default
3
85.24
0.780
0.868
0.562
0.653
0.887
SVM
RBF default
4
83.79
0.725
0.862
0.533
0.615
0.868
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.
