## 3 Results
The correlation matrix is a matrix that shows the linear relationship between each variable and is generated using Pearson's correlation coefficient. For each pair of variables, a correlation coefficient is calculated and the correlation coefficient ranges from \(-1\) to 1, where 1 indicates a perfect positive correlation, \(-1\) indicates a perfect negative correlation, and 0 indicates no correlation. Survivors are shown in blue, non-survivors in orange. Age is an important factor analyzed in the [PERSON] dataset, but it has been shown to be closely associated with other physiological factors. The plots reveal that non-survivors tend to have higher glucose levels and BMI, as well as slightly elevated heart rates, compared to survivors. These trends suggest that metabolic and cardiovascular parameters interact with age to influence long-term survival outcomes, emphasizing the need for multidimensional risk assessment. Figure 4 consists of three subfigures that collectively illustrate how individual features contribute to the prediction of mortality risk using the XGBoost (XGB) model, interpreted via SHAP (SHapley Additive exPlanations) values. Figure 4a shows a SHAP dependence plot, which visualizes how the value of a particular feature affects the SHAP value (i.e., the impact on model output), often in relation to another feature. This allows for the detection of interactions between variables and highlights non-linear relationships that the model has learned. Figure 4b presents a bar plot of the mean absolute SHAP values for each feature. This graph ranks the features in descending order of their overall importance, providing a straightforward way to identify which variables most strongly influence the model's predictions across the entire dataset. Figure 4c is a SHAP summary plot, which shows the distribution of SHAP values for each feature across all samples. Each dot represents a single prediction (i.e., a subject), and the color gradient indicates the feature value (e.g., high or low). This plot allows for a comprehensive overview of both the importance and the direction of influence (positive or negative) of each feature. SHAP is a game-theoretic approach to explain the output of machine-learning models, particularly useful for tree-based models like XGBoost, which are often considered \"black boxes.\" SHAP assigns an importance value to each feature for a given prediction by calculating the average marginal contribution of that feature across all possible feature combinations. This method enables a detailed, interpretable breakdown of model decisions, allowing researchers and clinicians to understand why a specific prediction was made and which features had the greatest influence. Such interpretability is essential when applying machine-learning models in sensitive domains like healthcare, where trust and transparency are critical. The reported results for Random Forest and SVM models, showing high accuracy (0.700) but zero values for precision, recall, F1 score, and sensitivity, are correct and commonly occur in imbalanced datasets. In this study, the target event (CVD) has a prevalence of approximately 25%, meaning that class 0 (non-CVD) dominates. If a model predicts all cases as class 0, it can achieve 70% accuracy simply by guessing the majority class. However, such a model completely fails to identify any true positives (CVD cases), resulting in zero recall, precision, and F1 score. Specificity is 1.0 because all negative cases are correctly identified. The AUC values of 0.642 and 0.500 indicate that, although the models fail to classify positive cases at a threshold of 0.5, their predicted probabilities still offer some (or random-level) discriminatory power. These results highlight the importance of addressing class imbalance through techniques like SMOTE, under-sampling, threshold tuning, or using class weights. Furthermore, relying solely on accuracy is misleading in imbalancedscenarios, and metrics such as AUC-ROC, AUPRC, and F1 score are more informative for performance evaluation.