## 2 Related Work  
Machine learning (ML) has become crucial in medical diagnosis, especially during the COVID-19 pandemic. A common challenge involves handling imbalanced datasets, where negative cases significantly outnumber positive ones, leading to model bias and reduced accuracy. Various methods, such as resampling, ensemble learning, and cost-sensitive algorithms, have been developed to address this issue. This section reviews relevant studies on handling class imbalance in medical datasets, with a focus on COVID-19 diagnosis, highlighting the impact of different ML techniques on improving diagnostic performance. ML enhances healthcare by improving the precision and efficiency of medical services. It can analyze large datasets to identify patterns and make predictions, supporting disease prediction, medical imaging, diagnostics, drug discoveries, and medical record management [16, 17]. In the context of COVID-19 diagnosis, ML applications have been explored in various settings. For example, a systematic review by [18] highlights AI techniques, including ML, for diagnostic purposes. Another study showed enhanced diagnostic accuracy, suggesting ML models may help reduce false negatives [19]. Moreover, a case study employing the XGBoost technique showed the effectiveness of hyperparameters and data preprocessing in building accurate predictive models for COVID-19 diagnosis [20, 21]. In another study [22], the authors examined how imbalanced data affect the performances of deep learning models when diagnosing COVID-19 using CT scans [23]. In [24], the authors evaluated various ML methods for diagnosing COVID-19 using chest X-ray images, finding that ensemble models achieved greater accuracy compared to individual models. Similarly, in [25], the authors investigated the impact of feature extraction techniques on the performance of a deep learning model for COVID-19 diagnosis [16]. Machine learning is also applied to predict COVID-19 diagnoses based on symptoms. Several studies have used this approach to enhance prediction accuracy by analyzingsymptons to identify patterns that help distinguish COVID-19 from other illnesses, showing promising results [26; 27]. They offered useful insights into the potential of ML for symptom-based COVID-19 prediction. The issue of imbalanced data poses a major challenge in healthcare, where some conditions are rare, causing datasets to have much more data for common cases than for rare ones. This imbalance can make ML models favor the majority class, leading to poor performance in detecting the minority class, which often represents critical medical conditions [28; 29]. To address the class imbalance, several methods are discussed in the literature. These include (1) resampling, which balances class distribution by oversampling or undersampling [30]; (2) cost-sensitive learning, which assigns a cost to misclassification [31; 32; 33]; and (3) ensemble methods, where multiple models are combined to improve prediction performance [31; 34; 35; 36; 37]. Regarding imbalanced data in COVID-19, one study proposed a privacy-preserving learning algorithm that addresses data imbalance constraints in such datasets [23; 38]. Another study by [39] examined the impact of imbalance on rapid COVID-19 antigen test performance. These studies demonstrate the effectiveness of algorithms across different COVID-19 data contexts [40; 41]. Recent advancements in handling class imbalance in medical datasets, particularly during the COVID-19 pandemic, have been significant. Studies such as [42] have highlighted innovative sampling techniques and algorithmic adjustments used during the initial phase of the pandemic, focusing on diagnostic and decision-making processes. Another study [43], explores optimal sample pooling methods under varying assumptions about true prevalence, providing valuable insights into prevalence estimation.