## 2 Materials and Methods
DA was first performed on both training and test data using the bootstrap sampling technique at 100% replication. The purpose of bootstrapping was to increase the overall sample size by resampling both classes with replacement while maintaining the original feature distributions. Second, the SMOTE was applied to the minority class within the bootstrapped data to generate synthetic samples and improve class balance in the training data. The SMOTE was not performed on the test dataset in order to maintain an imbalanced representation of the real-world data distribution. We performed 5-fold stratified cross-validation to evaluate model performance on training data. Figure 2 illustrates the stratified 5-fold cross-validation approach used. From Figure 2, the dataset was partitioned into five mutually exclusive and approximately equal-sized subsets called folds. During each iteration, four folds were used for model training while the remaining fold served as the validation set. This process was repeated five times, with each fold serving once as the validation set and four times as part of the training set. Performance metrics based on F1 score were computed independently for each iteration, and the overall performance of the model was obtained by averaging the metrics across all five folds. Six different machine learning models were evaluated: logistic regression (LR), support vector machine (SVM), three decision tree ensemble methods (random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB)), and an artificial neural network (ANN) model. The models were tuned with different hyperparameter settings based on several iterations of empirical testing and recommendations from prior research. The tuning process was guided by iterative experimentation and established recommendations from prior research. While we did not employ an automated grid or random search approach, hyperparameters were adjusted through multiple empirical runs using 5-fold stratified cross-validation to balance overfitting and underfitting. Table 3 presents the final hyperparameter adjustments made to each model type. The LR model was trained with a maximum iteration threshold of 500 (max_iter = 500) to permit sufficient transformation space for convergence, given the possibility of slow convergence in high-dimensional data. The maximum iteration of the SVM was also set to 500, with a radial basis function (RBF) kernel, which is well suited for capturing non-linear decision boundaries. The RF model was configured with 100 decision trees (n_estimators = 100). A maximum tree depth of 10 (max_depth = 10) was set to control overfitting. Additionally, a minimum requirement of five samples was allowed to split an internal node (min_samples_split = 5), which promoted generalization by restricting overly specific splits. The GBM model was set to perform 50 boosting iterations (n_estimators = 50) with each base learner constrained to a maximum tree depth of 5 (max_depth = 5). Similar to the RF model, a minimum sample split of 5 was set. The XGB hyperparameters were adjusted similarly to the GBM (n_estimators = 50, max_depth = 5, min_samples_split = 5). The XGB algorithm further leverages regularization and parallel processing to optimize performance and computational efficiency. A multilayer perceptron (MLP) was used, and \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Hyper Parameters** \\ \hline LR & max\_iter = 500 \\ SVM & max\_iter = 500, kernel = rbf \\ RF & n\_estimators = 100, max\_depth = 10, min\_samples\_split = 5 \\ GBM & n\_estimators = 50, max\_depth = 5, min\_samples\_split = 5 \\ XGB & n\_estimators = 50, max\_depth = 5, min\_samples\_split = 5 \\ ANN & optimizer = Adam, learning\_rate = 0.0005 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameter adjustments used for machine learning algorithm optimization. Figure 2: A diagram demonstrating the 5-fold cross-validation technique. the ANN algorithm was designed with three hidden layers.