Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations
Table 3
Top 10 models developed on original dataset 1.
Setting
Feature set
Accuracy
Sensitivity
Specificity
Precision
F-score
AUC
Bayesian network
Default
2
91.12
64.7
96.2
76.4
0.701
0.914
CHIAD
Default
2
90.76
54
97.8
82.6
0.653
0.909
MLP
2.5.5 boosting
1
90.63
53.6
97.7
81.5
0.647
0.904
MLP
Boosting 1.10
3
90.79
54
97.8
82.3
0.652
0.903
C5
Boosting
2
90.7
56.4
97.3
79.9
0.662
0.901
MLP
2.10.10
2
90.55
53.4
97.7
81.5
0.646
0.901
MLP
2.5.5
1
90.31
55.4
97
77.6
0.646
0.901
RF
Default
2
84.52
77.5
85.9
51.3
0.617
0.9
MLP
2.20.20
3
90.51
53.6
97.5
80.5
0.643
0.899
Bayesian network
Default
1
90.46
55.5
97.1
78.5
0.65
0.899
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.
