## 2 Materials and Methods
### Machine-Learning Analysis  
To identify key risk factors associated with cardiovascular-related mortality and evaluate predictive performance, the following five machine-learning algorithms were applied:  
* XGBoost Gradient boosting algorithm known for its high predictive accuracy and ability to handle complex interactions between variables. * Random Forest Ensemble learning method that constructs multiple decision trees and outputs the mode of the classes (classification) or mean prediction (regression). * Logistic Regression Traditional statistical model used as a baseline for binary classification tasks. * Weighted Ensemble Combines multiple model predictions by assigning different weights based on each model's performance. * LightGBM A fast, efficient gradient boosting framework that uses histogram-based algorithms and is optimized for speed and memory usage. * Neural Network A machine-learning model inspired by the human brain, capable of capturing complex non-linear relationships in the data. * Gradient Boosting An ensemble technique that builds models sequentially, where each new model corrects the errors of the previous ones. * k-Nearest Neighbors (kNN) A non-parametric method that classifies instances based on the majority label of their k closest neighbors in the feature space. * Support Vector Machine (SVM) A supervised learning algorithm that finds the optimal hyperplane to separate classes with maximum margin. For machine-learning classification, the following papers were used as references: [PERSON] et al. (2018). Each machine-learning model was trained using the designated training dataset, ensuring that the model learned patterns and relationships within the data while avoiding overfitting. The performance of each trained model was then rigorously evaluated on an independent test dataset, which was kept separate from the training data to provide an unbiased measure of generalization ability. To assess the predictive capability of the models, the area under the receiver operating characteristic curve (AUC-ROC) was selected as the primary evaluation metric. The AUC-ROC measures the model's ability to distinguish between positive and negative outcomes across different decision thresholds, providing a comprehensive assessment of classification performance. Higher AUC values indicate better discrimination between cases and controls. Additionally, feature importance analysis was conducted to determine the most influential predictors contributing to the model's decision-making process. Depending on the model type, different approaches were used to evaluate feature importance. For tree-based models such as Random Forest and XGBoost, the importance scores were derived from measures like Gini impurity or gain-based attribution. For linear models such as logistic regression, feature coefficients were examined to assess the relative impact of each predictor variable on the outcome. As multiple models were used in this study, the importance of the features may differ for each algorithm. To optimize model performance, hyperparameter tuning was performed using a systematic grid search approach in combination with k-fold cross-validation. The grid search method involved testing multiple combinations of hyperparameters to identify the optimal settings for each model. During cross-validation, the training dataset was divided into k equal subsets, with the model trained on k-1 folds and validated on the remaining fold in an iterative process. This method reduced the risk of overfitting while ensuring robust hyperparameter selection. The best-performing hyperparameter set was then applied to train the final model. Once the optimal model configurations were determined, a final evaluation was conducted using the independent test set. This ensured that the reported performance metrics reflected the model's true predictive capability without bias from the training or validation phases.