## 3 Materials and Methods
### Data Collection and Preprocessing  
Data collection is not considered a contribution of this study. The raw data used in this study was collected by the public health agency of Campina Grande, located in the Paraiba State of Northeast Brazil. This public agency receives information from all COVID-19 tests conducted in Campina Grande [8]. To ensure patient privacy, the agency's staff removed patient identification details, and the de-identified data were then made available for reuse in this study. The raw dataset contains various categorical features, including information about health professionals, security professionals, ethnicity, test types, symptoms (e.g., fever, sore throat, dyspnea, olfactory disorders, cough, coryza, taste disorders, and headache), additional symptoms, test results, comorbidities, test status, and symptom descriptions. Therefore, raw data from 55,676 Brazilian individuals were preprocessed to establish new datasets containing information on symptomatic patients tested for COVID-19. Additionally, we gathered data from a sample of 14,570 Brazilian individuals, which included information about symptomatic patients who underwent testing for influenza. As for COVID-19, the tests encompassed Reverse Transcription Polymerase Chain Reaction (RT-PCR) and rapid tests (antibody and antigen). We sourced information from the OpenDatasus platform ([[https://opendatasus.saude.gov.br/dataset/srag-2021-a-2023](https://opendatasus.saude.gov.br/dataset/srag-2021-a-2023)]([https://opendatasus.saude.gov.br/dataset/srag-2021-a-2023](https://opendatasus.saude.gov.br/dataset/srag-2021-a-2023)), accessed on 10 October 2023) the Brazilian Ministry of Health provided for this dataset. We performed data preprocessing using the Python programming language. During this preprocessing stage, we applied string-matching algorithms to address inconsistencies in the raw dataset. One notable inconsistency we addressed was the presence of empty columns related to symptoms, as the same symptoms were available within a column designated for general symptom descriptions. We also excluded certain instances from the initial samples based on our predefined exclusion criteria. For the COVID-19 dataset, these criteria encompassed patients with incomplete tests or undefined final classifications (12,929 instances), duplicated instances (12,929 instances), outliers resulting from input errors (10,408 instances), test types that were not RT-PCR or rapid (771 instances), individuals with undefined gender (27 instances), and asymptomatic patients (11,269 instances). The exclusion of asymptomatic patients was necessary because the algorithms relied on demographic characteristics and symptom data for accurate processing. We applied the same criteria to the influenza dataset. For example, the raw dataset, which was unbalanced, contained 5954 instances. After filtering and removal, the dataset was reduced to 4212 instances. We filtered the data to generate new datasets, including patients tested for COVID-19 and influenza. This process ensures that a patient tested positive for COVID-19 also tested negative for influenza. However, we acknowledge that a patient can be infected with both diseases simultaneously, even though this scenario is more uncommon. We did not consider this scenario in our study. The following attributes were considered: respiratory distress, vomiting, saturation, fatigue, diarrhea, abdominal pain, gender, healthcare professional status, fever, sore throat, dyspnea, olfactory disorder, cough, runny nose, taste disorder, and headache. We formulated six preprocessed datasets by combining COVID-19 and influenza data: unbalanced RT-PCR, balanced RT-PCR, unbalanced rapid, balanced rapid, both unbalanced, and both balanced. Thus, during the preprocessing, we oversampled the data by applying the near-miss technique [16]. In the datasets, the numbers 0 and 1 denote positive and negative results for COVID-19, while 2 and 3 represent positive and negative outcomes for influenza.