## 3 Results
Table 4 focuses on evaluating model performance using AUPRC (Area Under the Precision-Recall Curve), which is particularly relevant for imbalanced datasets. The purpose of Table 4 is to compare the core algorithms directly. The Precision-Recall (PR) curve is a crucial visualization tool, especially when evaluating the performance of classification models on imbalanced datasets. Unlike the Receiver Operating Characteristic (ROC) curve, which can be overly optimistic on imbalanced data, the PR curve focuses on the positive class, providing a more insightful view of a model's ability to identify positive instances while minimizing false positives. The PR curve plots Precision against Recall at various classification thresholds. Precision (Positive Predictive Value), measures the proportion of true positive predictions among all positive predictions made by the model. On the PR curve, the Y-axis represents precision, and the X-axis represents recall. A point closer to the top-right corner of the plot (higher precision and higher recall) indicates better performance. The Area Under the Precision-Recall Curve (AUPRC), also often referred to as Average Precision (AP), is a single scalar value that summarizes the performance of a model across all possible thresholds. The AUPRC calculates the weighted mean of precision values achieved at each threshold, where the weight is the increase in recall from the previous threshold. A higher AUPRC value indicates a better model. An AUPRC of 1.0 represents a perfect classifier that achieves 100% precision and 100% recall. Table 5 presents the comparative performance of the benchmark model, XGBoost, against all other machine-learning models using the DeLong test, which statistically assesses differences between two ROC (Receiver Operating Characteristic) curves. Each row compares the AUC (Area Under the Curve) of XGBoost (AUC\({}_{1}\)) with that of another model (AUC\({}_{2}\)), along with the following: \(\Delta\) AUC: the difference in AUC scores; Z: the Z-score from the DeLong test; \(p\)-value: the raw statistical significance; Adj. P: the adjusted \(p\)-value using Bonferroni correction to account for multiple comparisons; Effect Size: the magnitude of the difference between models, based on \(\Delta\) AUC. Among the comparisons, XGBoost significantly outperformed Random Forest, kNN, Neural Network, SVM, and Logistic Regression. These differences were statistically significant (\(p<0.001\)) and remained so even after adjusting for multiple testing. The largest performance gap was between XGBoost and Random Forest (\(\Delta\) AUC = 0.3341) with a large effect size of 0.758, indicating that XGBoost is markedly better at distinguishing between positive and negative cases than Random Forest. The difference between XGBoost and LightGBM or Gradient Boosting was not statistically significant, suggesting that these models had a performance close to XGBoost. After Bonferroni correction, six out of ten comparisons remained significant, highlighting that XGBoost offers consistently superior performance across a variety of models. This comprehensive analysis strengthens the claim that XGBoost is the most reliable and robust model in this mortality risk prediction task, especially under class-imbalanced conditions.