Table 2 Different evaluation metric values for the different machine learning models from Step 1 of Experiment 1.1. The highest value for each column (aka. evaluation metric) is boldfaced.
From: Exploring a global interpretation mechanism for deep learning networks when predicting sepsis
|  | Accuracy (%) | Precision (%) | Specificity (%) | Recall/sensitivity (%) | F1 score | Mathew’s coefficient |
|---|---|---|---|---|---|---|
Conv1D | 81.5 | 74.4 | 63.8 | 63.8 | 0.66 | 0.42 |
RF | 99.0 | 92.5 | 99.9 | 56.3 | 0.70 | 0.92 |
SVM | 85.0 | 9.7 | 85.0 | 74.4 | 0.17 | 0.23 |
AdaBoost | 88.0 | 10.2 | 88.3 | 60.8 | 0.17 | 0.21 |
MSC | 67.5 | 50.5 | 66.3 | 69.8 | 0.58 | 0.34 |
