## 3 Material and Methods
### Cost-Sensitive Learning  
Cost-sensitive learning [69] is an advanced strategy focusing on classifiers that consider the varying costs of different types of misclassification. This method is especially beneficial for imbalanced datasets, where one class is significantly underrepresented compared to others. Conventional models often struggle with minority classes, leading to serious misclassification errors. Table 5 presents an overview of two examples of cost-sensitive techniques.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Technique** & **Description** \\ \hline Adaptive boosting (AdaBoost) [68] & A boosting algorithm aimed at binary classification that combines several weak classifiers into a strong classifier to enhance overall performance. \\ \hline Bootstrap aggregating (bagging) & This technique improves model stability and accuracy by reducing variance and preventing overfitting. It creates multiple training data subsets through random sampling with replacement and trains separate instances of the same model on these subsets. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of Ensemble Learning Techniques.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Algorithm** & **Description** \\ \hline Support vector machine (SVM) [63] & A supervised method that separates data into different classes using a hyperplane. It aims to maximize the distance between the hyperplane and the nearest points from each class (support vectors). If data are not linearly separable, kernels are used to transform the data to higher dimensions. \\ \hline Multilayer perceptron (MLP) [62; 64] & A type of neural network with three layers: input, hidden, and output. Neurons process signals from the previous layer, and each output neuron indicates a specific class. The challenge involves determining the optimal number of hidden layers to avoid overfitting or underfitting. \\ \hline Naïve Bayes [65] & A probabilistic algorithm based on Bayes’ theorem. It assumes that all features are conditionally independent given the class, making it simple but effective for many tasks. \\ \hline Decision tree [66] & A binary tree where each node represents a feature and each branch represents a possible value. The tree splits the data at each level, selecting the feature that best divides the dataset until all instances are classified. \\ \hline XGBoost [67] & An ensemble method using decision trees and gradient boosting. It improves predictions by learning from previous errors. XGBoost is a more efficient version of gradient boosting, utilizing parallel computing for better performance. \\ \hline \hline \end{tabular}
\end{table}
Table 3: A summary of machine learning algorithms used for classification.  
Finally, we employed ensemble and cost-sensitive approaches to address the challenges of an imbalanced dataset. AdaBoost and bagging were used to enhance model performance. AdaBoost improved sensitivity by focusing on difficult-to-classify cases, while bagging reduced variance and prevented overfitting by using multiple resampled models. Additionally, cost-sensitive methods such as balanced random forest (CS RF) and balanced bagging (CS bagging) addressed class imbalance by assigning greater weight to minority samples, which increased recall for positive COVID-19 cases and minimized misclassification rates. Together, these techniques strengthened model reliability, supporting its applicability in COVID-19 diagnosis and risk assessment for diverse patient populations.