Table 4 Comparison on predictive performance of three alternative machine learning classification algorithms using the same features under the full model
Algorithm | Full model* (based on 30 features) | ||||
|---|---|---|---|---|---|
On Day 1 of admission | |||||
Sensitivity | Specificity | Accuracy (%) | |||
Macro averaged (%) | Micro averaged (%) | Macro averaged (%) | Micro averaged (%) | ||
Decision Tree # | 76.1 | 90.4 | 78.3 | 90.4 | 90.4 |
Random forest # | 77.8 | 90.9 | 69.6 | 90.9 | 90.9 |
As compared against the study’s chosen model | |||||
XGBoost | 82.6 | 92.3 | 96.0 | 96.1 | 92.3 |
Algorithm | Full model* (based on 30 features) | ||||
|---|---|---|---|---|---|
On Day 5 of admission | |||||
Sensitivity | Specificity | Accuracy (%) | |||
Macro averaged (%) | Micro averaged (%) | Macro averaged (%) | Micro averaged (%) | ||
Decision Tree # | 91.3 | 97.1 | 98.0 | 97.1 | 97.1 |
Random forest # | 92.3 | 97.6 | 95.3 | 97.6 | 97.6 |
As compared against the study’s chosen model | |||||
XGBoost | 99.7 | 99.5 | 99.5 | 99.5 | 99.5 |