## 2 Materials and Methods
### Comparative Evaluation with Traditional Feature Selection Methods and Classifiers  
Feature selection is a fundamental step in machine learning and data analysis, designed to enhance model performance, improve interpretability, and reduce computational complexity by identifying the most informative features while eliminating redundant or irrelevant ones. Feature selection methods are generally categorized into the following three main types: filter methods, wrapper methods, and embedded methods, each with distinct advantages and applications [40]. Filter methods assess feature relevance using statistical measures, selecting features independently of any specific model. These methods evaluate the relevance of features based on statistical or information-theoretic measures without involving any machine learning model. Statistics-based methods include Pearson correlation, Spearman correlation, Chi-square test, and Anova (analysis of variance) [41]. Information-theory-based methods are Information Gain (IG) [42], Minimum Redundancy Maximum Relevance (MRMR) [43], Conditional Mutual Information Maximization (CMIM), and Fast Correlation-Based Filter (FCBF) [44; 45]. Also, ranking-based methods like Select K Best (SKB) and variance threshold are among the widely used filter methods. The advantages of filter methods are their being computationally efficient and scalable to high-dimensional datasets. Also, independent of the learning algorithm, the selected features are generalizable across different models. These methods reduce overfitting by removing irrelevant or redundant features early in the pipeline. However, these methods ignore feature dependencies and interactions and may not always lead to the best feature subset for a specific model. Their selection is based on individual feature relevance rather than on assessing their collective impact. Wrapper methods evaluate subsets of features by iteratively testing their impact on model performance by using a specific learning algorithm [46]. Recursive Feature Elimination (RFE) is one of the popular approaches. While this method often yields high accuracy, it can be computationally intensive, especially for large datasets. Recursive Cluster Elimination (RCE) is another wrapper method [47]. RCE integrates clustering with recursive elimination to enhance classification performance and iteratively removes less informative feature clusters while training an SVM model. To improve predictive accuracy, RCE selects only the most relevant features, and discriminative features are retained [48]. Wrapper methods can capture feature dependencies and interactions, leading to potentially higher accuracy, and can be optimized for a specific learning algorithm, improving predictive performance. However, they are computationally expensive, especially for large datasets, and prone to overfitting, as they tailor feature selection to a specific model [46]. It is also worth noting that they require extensive cross-validation to avoid biased results. Embedded methods integrate feature selection directly into the model training process, allowing the algorithm to determine the most important features as it learns [49]. Examples include decision-tree-based methods, such as RF feature importance and XGBoost feature selection [50]. These methods strike a balance between efficiency and predictive performance. Embedded methods are more efficient than wrapper methods, as feature selection is performed during model training; moreover, they balance feature selection and predictive performance and are less prone to overfitting compared to wrapper methods. However, they are model-dependent. In other words, the selected features may not generalize well across different models. They can also be computationally demanding for complex models. Limited flexibility in applying selection criteria compared to filter methods creates a disadvantage for the model [40]. Several metagenomic data analysis studies have been performed using machine learning algorithms [51; 52]. [PERSON] et al.