## 3 Results
### Prediction of the Mortality
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Terms** & **Dataset** & **Method** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \multirow{8}{*}{3 Days} & \multirow{8}{*}{Without} & GaussianNB & 0.4697 \(\pm\) 0.0217 & 0.4390 \(\pm\) 0.0548 & 0.4524 \(\pm\) 0.0348 & 0.7307 \(\pm\) 0.0095 \\  & & AdaBoost & 0.5628 \(\pm\) 0.0119 & 0.7263 \(\pm\) 0.0248 & 0.6340 \(\pm\) 0.0145 & 0.7763 \(\pm\) 0.0078 \\  & & Bagging & 0.5143 \(\pm\) 0.0110 & 0.7166 \(\pm\) 0.0247 & 0.5986 \(\pm\) 0.0123 & 0.7551 \(\pm\) 0.0065 \\  & & Gradient Boosting & 0.5800 \(\pm\) 0.0102 & 0.7246 \(\pm\) 0.0210 & 0.6440 \(\pm\) 0.0061 & 0.7958 \(\pm\) 0.0045 \\  & & MLP Classifier & 0.5644 \(\pm\) 0.0099 & 0.6776 \(\pm\) 0.0156 & 0.6156 \(\pm\) 0.0041 & 0.7843 \(\pm\) 0.0033 \\  & & LightGBM & 0.7237 \(\pm\) 0.0084 & 0.6753 \(\pm\) 0.0164 & 0.6984 \(\pm\) 0.0051 & 0.8514 \(\pm\) 0.0018 \\  & & XGBoost & 0.7617 \(\pm\) 0.0140 & 0.6691 \(\pm\) 0.0194 & 0.7120 \(\pm\) 0.0048 & 0.8620 \(\pm\) 0.0016 \\ \cline{2-7}  & \multirow{8}{*}{With} & GaussianNB & 0.4360 \(\pm\) 0.0086 & 0.6197 \(\pm\) 0.0854 & 0.5100 \(\pm\) 0.0364 & 0.7032 \(\pm\) 0.0056 \\  & & AdaBoost & 0.5504 \(\pm\) 0.0153 & 0.7599 \(\pm\) 0.0299 & 0.6382 \(\pm\) 0.0184 & 0.7876 \(\pm\) 0.0073 \\  & & Bagging & 0.5339 \(\pm\) 0.0203 & 0.7278 \(\pm\) 0.0346 & 0.6157 \(\pm\) 0.0220 & 0.7638 \(\pm\) 0.0098 \\  & & Gradient Boosting & 0.5812 \(\pm\) 0.0216 & 0.7546 \(\pm\) 0.0333 & 0.6563 \(\pm\) 0.0219 & 0.7995 \(\pm\) 0.0108 \\  & & MLP Classifier & 0.5591 \(\pm\) 0.0528 & 0.7185 \(\pm\) 0.0316 & 0.6259 \(\pm\) 0.0204 & 0.7855 \(\pm\) 0.0251 \\  & & LightGBM & 0.7636 \(\pm\) 0.0291 & 0.7224 \(\pm\) 0.0224 & 0.7419 \(\pm\) 0.0153 & 0.8693 \(\pm\) 0.0067 \\  & & XGBoost & 0.7918 \(\pm\) 0.0240 & 0.7115 \(\pm\) 0.0240 & 0.7491 \(\pm\) 0.0162 & 0.8760 \(\pm\) 0.0071 \\ \hline \multirow{8}{*}{30 Days} & \multirow{8}{*}{Without} & GaussianNB & 0.4029 \(\pm\) 0.0169 & 0.4342 \(\pm\) 0.0329 & 0.4166 \(\pm\) 0.0078 & 0.7542 \(\pm\) 0.0087 \\  & & AdaBoost & 0.4259 \(\pm\) 0.0136 & 0.6919 \(\pm\) 0.0180 & 0.5272 \(\pm\) 0.0151 & 0.7590 \(\pm\) 0.0047 \\  & & Bagging & 0.4041 \(\pm\) 0.0018 & 0.7191 \(\pm\) 0.0174 & 0.5174 \(\pm\) 0.0060 & 0.7394 \(\pm\) 0.0052 \\  & & Gradient Boosting & 0.4411 \(\pm\) 0.0163 & 0.6870 \(\pm\) 0.0282 & 0.5372 \(\pm\) 0.0199 & 0.7701 \(\pm\) 0.0088 \\  & & MLP Classifier & 0.4387 \(\pm\) 0.0384 & 0.6503 \(\pm\) 0.0094 & 0.5228 \(\pm\) 0.0262 & 0.7684 \(\pm\) 0.0221 \\  & & LightGBM & 0.6127 \(\pm\) 0.0169 & 0.5747 \(\pm\) 0.0135 & 0.5931 \(\pm\) 0.0150 & 0.8468 \(\pm\) 0.0048 \\  & & XGBoost & 0.6481 \(\pm\) 0.0150 & 0.5498 \(\pm\) 0.0279 & 0.5947 \(\pm\) 0.0218 & 0.8547 \(\pm\) 0.0031 \\ \cline{2-7}  & \multirow{8}{*}{With} & GaussianNB & 0.3906 \(\pm\) 0.0229 & 0.5271 \(\pm\) 0.0461 & 0.4478 \(\pm\) 0.0263 & 0.7633 \(\pm\) 0.0129 \\  & & AdaBoost & 0.4479 \(\pm\) 0.0291 & 0.7101 \(\pm\) 0.0159 & 0.5491 \(\pm\) 0.0259 & 0.7691 \(\pm\) 0.0084 \\  & & Bagging & 0.4410 \(\pm\) 0.0232 & 0.7404 \(\pm\) 0.0194 & 0.5522 \(\pm\) 0.0159 & 0.7622 \(\pm\) 0.0091 \\  & & Gradient Boosting & 0.4720 \(\pm\) 0.0265 & 0.7135 \(\pm\) 0.0205 & 0.5674 \(\pm\) 0.0168 & 0.7847 \(\pm\) 0.0062 \\  & & MLP Classifier & 0.4473 \(\pm\) 0.0166 & 0.6904 \(\pm\) 0.0695 & 0.5411 \(\pm\) 0.0230 & 0.7690 \(\pm\) 0.0147 \\  & & LightGBM & 0.6627 \(\pm\) 0.0326 & 0.5945 \(\pm\) 0.0087 & 0.6265 \(\pm\) 0.0176 & 0.8598 \(\pm\) 0.0032 \\  & & XGBoost & 0.6976 \(\pm\) 0.0344 & 0.5721 \(\pm\) 0.0115 & 0.6279 \(\pm\) 0.0070 & 0.8658 \(\pm\) 0.0040 \\ \hline \multirow{8}{*}{365 Days} & \multirow{8}{*}{Without} & GaussianNB & 0.3116 \(\pm\) 0.0081 & 0.4043 \(\pm\) 0.0254 & 0.3514 \(\pm\) 0.0064 & 0.7859 \(\pm\) 0.0115 \\  & & AdaBoost & 0.2953 \(\pm\) 0.0206 & 0.6027 \(\pm\) 0.0316 & 0.3963 \(\pm\) 0.0252 & 0.7486 \(\pm\) 0.0125 \\ \cline{1-1}  & \multirow{8}{*}{Without} & Bagging & 0.2968 \(\pm\) 0.0147 & 0.6715 \(\pm\) 0.0295 & 0.4116 \(\pm\) 0.0197 & 0.7373 \(\pm\) 0.0096 \\ \cline{1-1}  & & Gradient Boosting & 0.3001 \(\pm\) 0.0259 & 0.6051 \(\pm\) 0.0375 & 0.4012 LightGBM and XGBoost performed best, achieving the highest accuracy (LightGBM, 0.8693; XGBoost, 0.8760) and F1-scores (LightGBM, 0.7419; XGBoost, 0.7491).