## 2 Methods
### Statistical Analysis  
Continuous variables were tested for normality using the Kolmogorov-Smirnov test. Normally distributed variables were summarized as the mean \(\pm\) standard deviation (SD), and group comparisons were conducted with \(t\)-tests. Continuous variables that did not follow a normal distribution were summarized as the median (interquartile range), and analyzed through the Mann-Whitney U test. Categorical variables were expressed as proportions (%), and comparisons were made using the chi-square test. A \(p\)-value < 0.001 indicated statistical significance. To ensure validity and robustness, variables demonstrating over 50% missingness were excluded from the analysis, including CRP, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, N-terminal pro-B-type natriuretic peptide, and troponin I. For features with 20-50% missingness, imputation was conducted using the median value, while features with less than 20% missing data underwent K-Nearest Neighbors (KNN) imputation. The distribution of missing data across all features is illustrated in Figure S1, which provides a visual summary of the missing data proportion. Features were selected using Least Absolute Shrinkage and Selection Operator (Lasso) regression and feature importance, based on RF. Both methods ranked the significance of variables, and the top 20% of features were selected for model development. Lasso regression is a linear model that combines regularization with feature selection. It minimizes the residual sum of squares, while adding a penalty proportional to the absolute values of the coefficients. RF is an ensemble learning method that inherently evaluates feature importance, and was utilized for selecting features. We employed various data resampling techniques to deal with the issue of class imbalance, including the Synthetic Minority Over-sampling Technique (SMOTE), the Borderline Synthetic Minority Over-sampling Technique (Borderline SMOTE), the Support Vector Machine Synthetic Minority Over-sampling Technique (SVMSMOTE), Adaptive Synthetic Sampling (ADASYN), Synthetic Minority Over-sampling Technique Edited Nearest Neighbors (SMOTEENN), and Synthetic Minority Over-sampling Technique Tomek Links (SMOTETomek). For each resampling method, we utilized the XGBoost classifier to build the model. The training process employed 10-fold cross-validation. The performance was assessed based on the test set, using accuracy, precision, recall, and F1 score. During the data resampling process, the parameters were set to their default values. Four ML algorithms were implemented for prediction: RF, Adaboost, XGBoost, and Gradient Boosting Decision Trees (GBDT). These methods were selected due to their strong predictive capabilities, robustness to overfitting, and effectiveness in handling structured data [28; 29; 30]. RF is a bagging method, while AdaBoost, GBDT, and XGBoost are boosting methods, which enables a comparison of the applicability of bagging and boosting to the study. RF is a technique that aggregates multiple decision trees to enhance model performance. It boosts accuracy and mitigates overfitting by combining the outputs of these trees, with the majority voting to make the final decision. Employing a boosting strategy, Adaboost integrates a series of base models (often decision stumps) into a unified predictive system via iterative reweighting, ensuring that each subsequent model compensates for the deficiencies of its predecessor. GBDT employs iterative gradient-based optimization to sequentially construct decision trees, with each successive predictor minimizing residual errors from preceding models. XGBoost is an optimized implementation of gradient boosting, designed for greater efficiency, scalability, and flexibility. The RF, Adaboost, and GBDT models were implemented using scikit-learn, while the XGBoost model was implemented using the XGBoost library. To avoid overfitting, we fine-tuned the model parameters to enhance generalization performance.