## 3 Results  
Among the machine-learning models used in the analysis, XGBoost demonstrated the highest performance in predicting mortality, with an area under the curve (AUC) value of 0.83 (Figure 1). Of the 15 parameters extracted from the Framingham dataset in this study, coronary artery disease (PREVCHD), blood glucose level (GLUCOSE), and diastolic blood pressure (DIABP) were identified as significant risk factors associated with increased mortality (Figures 2-4, Table 3). Table 4 and Figure 5 highlights the XGBoost model's superior performance, achieving an AUPRC of 0.7232, a significant 131.37% improvement over the baseline random classifier. This underscores its effectiveness in identifying positive instances while minimizing false positives compared to other models like LightGBM and Logistic Regression. The DeLong test was performed and the model comparison performance using the area under the receiver operating characteristic curve (AUC), 95% confidence intervals, and statistical significance tests are shown in Table 5. The DeLong test evaluates the significance of the difference in AUC and does not directly evaluate the difference in the overall shape of the ROC curve. Figure 1: Receiver Operating Characteristics (ROC). Figure 3: Plots of age, glucose, BMI, and heart rate for survived and non-survived. Figure 2: Refined correlation plot. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**AUPRC**} & \multirow{2}{*}{**Improvement Over**} \\  & & & **Baseline (\%)** \\ \hline Baseline (Random Classifier) & 0.3126 & – \\ \hline XGBoost & 0.7232 & 131.37\% \\ \hline LightGBM & 0.7202 & 130.44\% \\ \hline Logistic Regression & 0.7093 & 126.95\% \\ \hline Neural Network & 0.7054 & 125.69\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AUPRC (Area Under the Precision–Recall Curve) for each model. Figure 4: Predicting the risk of mortality for each factor. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **AUPRC** & **Improvement Over** \\  & **Baseline (\%)** \\ \hline Gradient Boosting & 0.6950 & 122.36\% \\ \hline SVM & 0.6563 & 109.97\% \\ \hline kNN & 0.6433 & 105.83\% \\ \hline Random Forest & 0.6166 & 97.28\% \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 5: Precision, area under the recall curve, AUPRC. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **AUC\({}_{1}\) (95\% CI)** & **AUC\({}_{2}\) (95\% CI)** & \(\Delta\) **AUC** & **Z** & **_p-Value_ & **Adj. P** & **Effect Size** \\ \hline XGBost vs. & 0.834 & 0.500 & & & & & \\ Random Forest & (0.814–0.854) & (0.480–0.520) & 0.3341 & 36.96 & \(<\)0.001 *** & \(<\)0.001 *** & 0.758 \\ \hline XGBost vs. & 0.834 & 0.766 & & & & & \\ kNN & (0.814–0.854) & (0.746–0.786) & 0.0683 & 8.45 & \(<\)0.001 *** & \(<\)0.001 *** & 0.171 \\ \hline XGBost vs. & 0.834 & 0.797 & & & & & \\ Neural & (0.814–0.854) & (0.777–0.817) & 0.0375 & 5.08 & \(<\)0.001 *** & \(<\)0.001 *** & 0.097 \\ \hline XGBost vs. & 0.834 & 0.815 & & & & & \\ SVM & (0.814–0.854) & (0.795–0.835) & 0.0191 & 3.36 & \(<\)0.001 *** & 0.018 * & 0.050 \\ \hline XGBost vs. & 0.834 & 0.822 & & & & & \\ Logistic & (0.814–0.854) & (0.802–0.842) & 0.0119 & 4.35 & \(<\)0.001 *** & \(<\)0.001 *** & 0.032 \\ Regression & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Benchmark model (XGBoost) vs. all other models. To further optimize the performance of the XGBoost model, which showed the highest predictive accuracy in our analysis, a hyperparameter tuning process was conducted using GridSearchCV.