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.
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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.