## 5 Discussion
Clinically, oversampling and synthetic techniques are preferred for COVID-19 detection, providing an optimal balance between true positive detection and false positive management, essential for early and accurate diagnoses. It is important to note that these techniques are specifically employed in the context of symptoms and comorbidities, thereby enhancing their diagnostic precision. Rapidly identifying patients with COVID-19 is essential not only for ensuring prompt treatment but also for enforcing isolation measures that help contain the virus's spread and reduce pressure on the healthcare system. Consequently, incorporating these techniques into clinical practice can greatly enhance healthcare providers' capacity to manage the COVID-19 pandemic effectively, especially when combined with assessments of patients' symptoms and underlying health conditions. The use of technology in healthcare does not replace human clinical decisions but rather serves as a complementary tool to support medical work [12]. Given the vast amount of information in the healthcare field, manual analysis can be inefficient and complex, making it important to obtain reliable knowledge from data to provide fast and reliable solutions [8; 11; 12]. In a clinical setting, sensitivity is a crucial concept, referring to the ability of a test to detect a disease, such as COVID-19, in the presence of the disease [76]. Artificial intelligence (AI) has been used as a sensitive and specific method for screening COVID-19 in patients with respiratory conditions, improving diagnostic and screening capacity [77]. ML has demonstrated its usefulness in providing accuracy in the healthcare field [78]. Incorporating ML models into clinical practice requires both high predictive accuracy and interpretability to ensure healthcare professionals can trust and utilize the model's outputs effectively. This study focuses on feature importance analysis to identify key symptoms and comorbidities (e.g., fever, cough, hypertension) driving model predictions. These insights allow clinicians to better understand the factors influencing the model's decisions, which is crucial for integrating machine learning tools into clinical workflows. In clinical practice, our model could serve as a decision support tool, helping healthcare providers prioritize patients based on symptom profiles and comorbidities. Interpretability enhances clinical utility by enabling healthcare professionals to make informed decisions, improving diagnostic accuracy and patient outcomes. Future work will validate these models in real-world settings to assess reliability and practical applicability. Our study addresses the challenges of using machine learning for COVID-19 detection, particularly in handling imbalanced datasets. While traditional methods like random oversampling and SMOTE are discussed for their effectiveness and limitations, we also explore the potential of ensemble and cost-sensitive techniques despite their computational demands. To further enhance diagnostic performance, recent advancements such as generative adversarial networks (GANs) and transfer learning are acknowledged as promising solutions for improving accuracy and robustness in unbalanced datasets, positioning our work within the broader scope of state-of-the-art healthcare informatics. This study acknowledges several limitations in its methods and results. Data imbalance, particularly within the EPIVIGILA dataset, presents a potential bias in model predictions. Although sampling techniques like SMOTE and ADASYN were applied to address this, further model tuning and validation may be needed to mitigate overfitting risks. Data preprocessing posed additional challenges, as handling missing values led to a high number of deleted instances, potentially impacting data representativeness; exploring advanced imputation techniques could help reduce data loss in future work.