Research Article
Machine Learning Models for Survival and Neurological Outcome Prediction of Out-of-Hospital Cardiac Arrest Patients
Table 2
Rank of parameter importance after stepwise parameter selection.
| Rank | LR | SVM | XGB |
| 1st | PCI | Troponin I | Troponin I | 2nd | Diabetes mellitus | CCI | Total epinephrine dose | 3rd | Hemoglobin | Dementia | Heart failure | 4th | Troponin I | Diabetes ketoacidosis | PCI | 5th | Dementia | PCI | Amiodarone use | 6th | CCI | Norepinephrine use | Calcium use | 7th | Norepinephrine use | ECMO | Dementia | 8th | Liver cirrhosis | Pulmonary embolism | Sodium bicarbonate use | 9th | Hypokalemia | Amiodarone use | Band neutrophil | 10th | Tumor metastasis | Pneumothorax | Malignancy | 11th | | Tumor metastasis | Acute myocardial infarction | 12th | | Acidosis | |
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LR: logistic regression; SVM: support vector machine; XGB: extreme gradient boosting; PCI: percutaneous coronary intervention; CCI: Charlson comorbidity index; ECMO: extracorporeal membrane oxygenation.
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