## 1 Introduction  
Millions of people have been affected by the COVID-19 worldwide crisis with an alarming number of confirmed cases and deaths [1]. Given this context, it is essential to ensure accurate diagnoses for effective management and control. Epidemic monitoring is very important for public health, as it involves the collection, analysis, and interpretation of data to accurately describe and survey emergencies in a timely manner. This process facilitates the planning, implementation, and evaluation of public health interventions and programs [2]. This underscores the urgent need for precise and rapid diagnostics to manage virus spread effectively. Quickly identifying and isolating cases is essential for controlling outbreaks and guiding health responses. In the case of COVID-19, symptom-based diagnostics [3] is a crucial tool for timely pandemic response, allowing healthcare providers to make an initial assessment and make decisions related to testing, treatment, and isolation without the need for laboratory confirmation. Timely and precise diagnosis is crucial to control the spread of a pandemic virus and minimize its impact, especially at the beginning. In this context, algorithms that assist with symptom-based diagnosis are essential for managing a large number of cases [4]. It is in this context that machine learning (ML) algorithms are essential for supporting medical diagnosis, as they aim for precise identification and classification of diseases within a dataset [5]. During pandemics like COVID-19, where time is critical and healthcare systems are stretched, ML models enable the rapid processing of vast amounts of real-time data. The literature presents several cases where ML has facilitated medical diagnosis, especially in the detection of complex diseases [6]. However, these diagnostic datasets are often imbalanced in the context of medical data [7]. In this context, most data represent healthy individuals, while instances of the disease of interest are comparatively few. This creates a problem, as learning models tend to be biased toward the majority dataset [8], resulting in poor performance when identifying the minority class, which is critical for accurate medical diagnosis. Several methods have been proposed to address imbalanced datasets, including sampling methods, ensemble learning, and cost-sensitive learning [9]. These techniques help manage class imbalance by providing various methodologies to enhance the performance of machine learning algorithms when faced with such data. In the COVID-19 domain, there are several cases highlighting class imbalance [10]. However, few studies focus on COVID-19 symptoms, indicating a significant need for research in this area. This underscores the necessity for additional research and the exploration of more alternatives for this specific application. Therefore, both symptoms and comorbidities are essential factors for performing quantitative analyses of prevalence, whether cases are confirmed or ruled out [11]. In 2020, the Chilean government launched the EPIVIGILA platform [12], a virtual monitoring system for tracking COVID-19 and implementing critical strategies for public health decision-making. This platform includes over 6 million patient records, providing a crucial data source for studying the detection and spread of COVID-19 in Chile and aiding in prevention and control measures at the national level [13]. However, this dataset is highly imbalanced, with only 10% of the data corresponding to confirmed cases of the disease, making it challenging to employ ML methods and complicating the use of the data to support diagnosis [14; 15]. By applying techniques to mitigate the imbalance, we aim to create more accurate and generalizable COVID-19 diagnosis models that can reliably identify all patient categories, leading to fairer and more effective diagnostic outcomes.