## 3 Results
### Model Evaluation and Comparison  
Feature selection was performed using two methods: Lasso regression and RF, with the top 20% of variables selected for subsequent analysis. Table S2 lists the selected features, ranked by importance, with APSIII, lactate, and RDW appearing in both methods. A comparative analysis of the six resampling techniques was conducted to evaluate their impact on model performance. The results are summarized in Table S3. Given the study's primary objective of mortality prediction, recall was prioritized as the key performance metric. SMOTEENN demonstrated superior recall performance (0.6379), leading to its selection as the optimal resampling method for handling class imbalance in our predictive model. Table 2 outlines the evaluation results for the models, incorporating metrics including RF, AdaBoost, GBDT, and XGBoost, evaluated using different feature selection methods. In \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Baseline Characteristics** & **All** & **Survival** & **Non-Survival** & _p-Value_ \\ \hline Race & & & & \\ Asian & 43 (2.06\%) & 37 (2.05\%) & 6 (2.14\%) & \\ Black & 159 (7.63\%) & 140 (7.76\%) & 19 (6.79\%) & \\ Hispanic/Latino & 77 (3.69\%) & 67 (3.71\%) & 10 (3.57\%) & 0.652 \\ Other & 64 (3.07\%) & 59 (3.27\%) & 5 (1.79\%) & \\ Unknown & 468 (22.45\%) & 397 (21.99\%) & 71 (25.36\%) & \\ White & 1274 (61.10\%) & 1105 (61.22\%) & 169 (60.36\%) & \\ ACEI & & & & \\ No & 1437 (68.92\%) & 1193 (66.09\%) & 244 (87.14\%) & \textless{}0.001 \\ Yes & 648 (31.08\%) & 612 (33.91\%) & 36 (12.86\%) & \\ History of congestive heart failure & & & & \\ No & 949 (45.52\%) & 859 (47.59\%) & 90 (32.14\%) & \textless{}0.001 \\ Yes & 1136 (54.48\%) & 946 (52.41\%) & 190 (67.86\%) & \\ History of peripheral vascular disease & & & & \\ No & 1743 (83.60\%) & 1516 (83.99\%) & 227 (81.07\%) & 0.254 \\ Yes & 342 (16.40\%) & 289 (16.01\%) & 53 (18.93\%) & \\ History of cerebrovascular disease & & & & \\ No & 1746 (83.74\%) & 1530 (84.76\%) & 216 (77.14\%) & 0.002 \\ Yes & 339 (16.26\%) & 275 (15.24\%) & 64 (22.86\%) & \\ History of chronic pulmonary disease & & & & \\ No & 1570 (75.30\%) & 1372 (76.01\%) & 198 (70.71\%) & 0.066 \\ Yes & 515 (24.70\%) & 433 (23.99\%) & 82 (29.29\%) & \\ History of renal disease & & & & \\ No & 1310 (62.83\%) & 1163 (64.43\%) & 147 (52.50\%) & \textless{}0.001 \\ Yes & 775 (37.17\%) & 642 (35.57\%) & 133 (47.50\%) & \\ History of diabetes & & & & \\ No & 1081 (51.85\%) & 919 (50.91\%) & 162 (57.86\%) & 0.036 \\ Yes & 1004 (48.15\%) & 886 (49.09\%) & 118 (42.14\%) & \\ Aspirin & & & & \\ No & 214 (10.26\%) & 146 (8.09\%) & 68 (24.29\%) & \textless{}0.001 \\ Yes & 1871 (89.74\%) & 1659 (91.91\%) & 212 (75.71\%) & \\ Antiplatelet drugs & & & & \\ No & 1458 (69.93\%) & 1261 (69.86\%) & 197 (70.36\%) & 0.922 \\ Yes & 627 (30.07\%) & 544 (30.14\%) & 83 (29.64\%) & \\ Statins & & & & \\ No & 378 (18.13\%) & 288 (15.96\%) & 90 (32.14\%) & \textless{}0.001 \\ Yes & 1707 (81.87\%) & 1517 (84.04\%) & 190 (67.86\%) & \\ \hline \hline \end{tabular} Note: values are displayed as median (interquartile range) or N (proportion). Angiotensin-converting enzyme inhibitors (ACEIs): Atrial fibrillation (AF); Acute physiology score III (APSIII); Body mass index (BMI); Diastolic blood pressure (DBP); Length of stay (LOS); Mean arterial pressure (MAP); Systolic blood pressure (SBP).  
\end{table} 
Table 1: _Cont._general, using all features yielded higher performance metrics compared to using Lasso-selected or RF-selected features. The models incorporating all features showed maximal predictive performance, with the RF model reaching the highest accuracy (0.8513), precision (0.9016), recall (0.8513), F1 score (0.8670), and AUC (8903), outperforming the other models. The accuracy for AdaBoost, GBDT, and XGBoost was 0.8201, 0.8657, and 0.8201, respectively. In terms of precision, all models performed well, with scores above 0.85. As for recall, AdaBoost performed the lowest, with a score of only 0.8201; meanwhile, a score of 0.8345 was achieved with GBDT and 0.8417 with XGBoost.