## 5 Results  
### Classifier Performance Comparison (RQ1)  
Table 3 summarizes the performance of four supervised learning models evaluated on the task of predicting sepsis readmissions. Each metric (AUROC, AUPRC, and MCC) is averaged over 200 bootstrap replications, providing a robust estimate of the models' generalization capabilities. CatBoost achieves the highest mean scores across all three metrics: 0.8506 \(\pm\) 0.05 in AUROC, 0.6807 \(\pm\) 0.09 in AUPRC, and 0.6028 \(\pm\) 0.09 in MCC. This suggests that CatBoost consistently balances the identification of true positives and true negatives while maintaining strong discrimination between classes and handling the class imbalance inherent to sepsis readmissions. AUROC measures the overall separability of the positive and negative classes by varying the decision threshold. CatBoost's AUROC surpasses those of XGBoost, Random Forest, and Decision Tree, indicating that it produces a better rank ordering of patients likely to be readmitted. The Friedman test (Figure 3) confirms that the differences among the four models are statistically significant (\(\chi_{\text{Friedman}}^{2}=314.6368,\ p=6.7562\times 10^{-68}\)). Post-hoc Nemenyi tests reveal that CatBoost holds a significant edge over all other methods, highlighting its superior capability to discriminate between sepsis patients who will and will not be admitted to ICU. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **AUROC** & **AUPRC** & **MCC** \\ \hline XGBoost & 0.8356 (\(\pm\)0.05) & 0.6567 (\(\pm\)0.09) & 0.5839 (\(\pm\)0.09) \\ CatBoost & **0.8506 (\(\pm\)0.05)** & **0.6807 (\(\pm\)0.09)** & **0.6028 (\(\pm\)0.09)** \\ Decision Tree & 0.7437 (\(\pm\)0.07) & 0.6340 (\(\pm\)0.10) & 0.5816 (\(\pm\)0.11) \\ Random Forest & 0.8306 (\(\pm\)0.06) & 0.6652 (\(\pm\)0.09) & 0.5828 (\(\pm\)0.10) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of Supervised Models Across 200 Bootstrap Samples. AUPRC focuses specifically on the model's performance for positive cases, making it a critical metric for domains with class imbalance. CatBoost again achieves the highest mean AUPRC, \(0.6807\pm 0.09\), which implies that it captures more of the actual admissions (true positives) at lower false-positive rates compared to competing methods. The [PERSON] statistic for AUPRC (Figure 4) (\(\chi^{2}_{\text{[PERSON]}}=91.41\), \(p=1.0906\times 10^{-19}\)) indicates large discrepancies between classifiers, and subsequent Nemenyi tests confirm that CatBoost's advantage is statistically significant. This result is clinically important: in sepsis treatment settings, minimizing missed positive cases is a priority because each undetected admission risk can lead to delayed interventions and adverse outcomes. The MCC consolidates true positives, false positives, true negatives, and false negatives into a single coefficient, offering a more balanced measure when class prevalence is skewed. Once again, CatBoost's average MCC of \(0.6028\pm 0.09\) exceeds those of the other algorithms, although the differences among models are more modest than for AUROC or AUPRC. The Friedman test (Figure 5) (\(\chi^{2}_{\text{Friedman}}=18.375\), \(p=3.6807\times 10^{-4}\)) and subsequent Nemenyi tests verify that CatBoost significantly outperforms Random Forest, XGBoost, and Decision Tree with respect to MCC. From a clinical standpoint, a higher MCC indicates more reliable predictions that avoid both over-diagnosis (unnecessary interventions) and under-diagnosis (missed sepsis relapses). Figure 4: Friedman-Nemenyi significance test results regarding AUPRC of machine learning models across 200 bootstrapping iterations. Figure 3: Friedman-Nemenyi significance test results regarding AUROC of machine learning models across 200 bootstrapping iterations. Three primary observations emerge from these results. First, boosting-based methods outperform single-tree and classical ensemble approaches in distinguishing high-risk sepsis patients.