## 3 Results
### Prediction of the Mortality  
In our study, we employed a 10-fold cross-validation method to construct a predictive model for type 2 diabetes patients, forecasting outcomes at three days, 30 days, and 365 days. The model utilized data collected within the 24 h preceding ICU admission, including the 365-day mortality rate. The dataset was partitioned, with 80% allocated for model training and 20% for testing, followed by comprehensive statistical analyses to evaluate its performance.  
Table 3 presents the AUROC scores for various classifiers in mortality prediction. Notably, for the 3-day mortality prediction, the bagging classifier achieved the highest AUROC score of 0.8112, surpassing other methods. In contrast, for the 30-day and 365-day mortality predictions, the Adaboost classifier obtained the highest AUROC scores of 0.7952  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Terms** & **Overall** \\ \hline General & 14,222 (100\%) \\ \hline
\begin{tabular}{l} Number \\ Age \\ Gender (male) \\ Insurance Type \\ Self-Pay \\ Government \\ Medicare \\ Medicaid \\ Private \\ Ethnicity \\ Asian \\ Black/African \\ Hispanic/Latino \\ White \\ Other \\ Marital Status \\ Married \\ Single \\ Divorced \\ Separated \\ Widowed \\ Unknown \\ Last ICU type \\ Coronary Care Unit \\ Cardiac Surgery Recovery Unit \\ Medical ICU \\ Surgical ICU \\ Trauma Surgical ICU \\ Outcomes \\ Hospital LOS (days) [Q1–Q3] \\ ICU LOS (days) [Q1–Q3] & 13.13 [8.13–18.17] \\ 6.74 [3.82–8.92) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected patient demographic information.  
and 0.7898, respectively. This highlights Adaboost's superior capability in distinguishing between patients who were readmitted and those who were not. Figure 3 depicts the ROC curves of the classifiers, visually illustrating their trade-offs between true positive and false positive rates. These curves effectively demonstrate the performance of each classifier across different thresholds. In the figure, the red dashed line represents the baseline for a random classifier, corresponding to an AUROC of 0.5. Figure 4 compares the AUROC scores for different machine learning methods across 3 days, 30 days, and 365 days, providing a clear visualization of their relative effectiveness.  
Table 4 provides a comprehensive summary of the performance of six machine learning models in predicting ICU patient mortality at 3, 30, and 365 days post-discharge, evaluated using precision, recall, F1 score, and accuracy. The results indicate that the GaussianNB model attained the highest accuracy for mortality prediction at all three of the time intervals considered in this study, as follows: 0.8832 at 3 days, 0.8742 at 30 days, and 0.8693 at 365 days. Adaboost followed, demonstrating the second-highest accuracy with values of 0.8766, 0.8256, and 0.8161 at 3 days, 30 days, and 365 days, respectively.  
Figure 3: The mortality ROC curves for the different classifiers.  
\begin{table}
\begin{tabular}{c c c} \hline
**Terms** & **Methods** & **AUROC** \\ \hline \multirow{8}{*}{3 Days} & Adaboost & 0.7997 \(\pm\) 0.0067 \\  & Bagging & 0.8112 \(\pm\) 0.0047 \\  & GaussianNB & 0.7275 \(\pm\) 0.0340 \\  & Logistic Regression & 0.7746 \(\pm\) 0.0143 \\  & MLP Classifier & 0.7993 \(\pm\) 0.0342 \\  & SVC & 0.8051 \(\pm\) 0.0146 \\ \hline \multirow{8}{*}{30 Days} & Adaboost & 0.7952 \(\pm\) 0.0114 \\  & Bagging & 0.7804 \(\pm\) 0.0068 \\  & GaussianNB & 0.7078 \(\pm\) 0.0124 \\  & Logistic Regression & 0.7698 \(\pm\) 0.0100 \\  & MLP Classifier & 0.7619 \(\pm\) 0.0029 \\  & SVC & 0.7704 \(\pm\) 0.0048 \\ \hline \multirow{8}{*}{365 Days} & Adaboost & 0.7898 \(\pm\) 0.0070 \\  & Bagging & 0.7686 \(\pm\) 0.0122 \\ \cline{1-1}  & GaussianNB & 0.7070 \(\pm\) 0.0082 \\ \cline{1-1}  & Logistic Regression & 0.7672 \(\pm\) 0.0108 \\ \cline{1-1}  & MLP Classifier & 0.7524 \(\pm\) 0.0079 \\ \cline{1-1}  & SVC & 0.7633 \(\pm\) 0.0067 \\ \hline \end{tabular}
\end{table}
Table 3: The mortality AUROC scores of various classifiers.