## 2 Materials and Methods
### Machine Learning Workflow  
To ensure data consistency, all variables were normalized using the StandardScaler library during both the feature selection (FS) and ML estimation phases. For feature selection, we employed Sequential Forward Feature Selection (SFFS) with 5-fold stratified cross-validation. SFFS is particularly advantageous because it offers a dynamic approach to feature selection, allowing for both inclusion and exclusion of features during the search process, which helps in identifying an optimal subset that improves the model's performance. This flexibility is crucial in explainable machine learning studies where the interpretability of selected features is as important as predictive accuracy. Additionally, SFFS tends to yield more reliable and robust feature sets compared to standard forward selection methods, which is essential for building models that not only perform well but also provide meaningful insights into the underlying data patterns. Specifically, this robust technique iteratively constructs a feature subset by adding one feature at a time, selecting those that optimally enhance model performance while maintaining balanced class distributions. Additionally, we performed undersampling during each run on the training data for both ML and FS to address class imbalance. The advantages of SFFS include improved predictive accuracy, as it ensures that the model not only improves in accuracy but also maintains robustness through stratified cross-validation, which is vital for handling imbalanced datasets. Additionally, the sequential addition of features helps identify the most relevant variables, reducing model complexity and the risk of overfitting. Furthermore, SFFS provides a comprehensive method tailored to the specific problem, leading to better model generalization and interpretability. For our binary classification task, we leveraged several advanced ML classifiers, including XGBoost, Support Vector Machine (SVM), and Logistic Regression (LR), combined with the SFFS FS algorithm. Specifically, XGBoost is chosen for its high performance, handling of non-linear relationships, and robustness against overfitting, making it excellent for complex datasets. SVM is selected for its effectiveness in high-dimensional spaces and its ability to model both linear and non-linear relationships through various kernels, offering flexibility. LR is used for its simplicity, interpretability, and ability to provide probabilistic outputs, serving as a strong baseline model. Hence, this multi-algorithm approach was chosen for several reasons. Firstly, it allows for robustness assessment by comparing results across different algorithms to ensure consistency. Secondly, it helps mitigate the algorithmic biases inherent in any single method. Thirdly, it assists in understanding the task's sensitivity to different features, which is crucial for the SFFS FS algorithm. Lastly, testing multiple algorithms increases the credibility and generalizability of our findings. Each classifier underwent the FS process separately. To ensure robust performance and minimize overfitting, we applied 5-fold stratified cross-validation for validation. Additionally, hyperparameter tuning was performed within the training set using 5-fold stratified cross-validation, optimizing the models for the best configurations. Specifically, hyperparameter tuning for each ML model was conducted using a grid search approach to identify the optimal set of parameters that maximized model performance. ForLR, a grid was defined over the hyperparameters \(C\) (regularization strength) and solver, exploring combinations such as C = [0.1, 1, 10, 100] and solvers including \"newton-cg\", \"lbfgs\", and \"liblinear\". For XGBoost, the tuning involved varying n_estimators (number of trees), max_depth (maximum tree depth), learning_rate, and min_child_weight, with grids set to [50, 100, 200], [3, 4, 5], [0.01, 0.1, 0.2], and [1, 2, 3], respectively.