Application of the KDD Process to COVID-19 Data: A Case Study in Rio Grande do Sul, Brazil
Abstract
Given the increasing amount of data linked to a complex healthcare system, challenges arise in enhancing decision-making based on data patterns. To address this issue, it is crucial to explore data mining as a tool to extract valuable insights. This study focuses on applying the Knowledge Discovery in Databases (KDD) process, particularly in the preliminary and data mining stages, to identify patterns in the COVID-19 pandemic data in Rio Grande do Sul, Brazil. Our analyses revealed interesting patterns, such as the association between specific symptoms and patient outcomes. While the results offer valuable insights, it is important to note that this study does not aim to provide definitive conclusions regarding the causal relationship between symptoms and patient outcomes. Instead, the goal is to present patterns identified in the data without interpreting their clinical significance. These findings have the potential to inform future research and provide a solid foundation for proactive decision-making in public health.
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