###### Abstract  
Data classification is an automatic or semi-automatic process that, utilizing artificial intelligence algorithms, learns the variable and class relationships of a dataset for use a posteriori in situations where the class result is unknown. For many years, work on this topic has been aimed at increasing the hit rates of algorithms. However, when the problem is restricted to applications in healthcare, besides the concern with performance, it is also necessary to design algorithms whose results are understandable by the specialists responsible for making the decisions. Among the problems in the field of medicine, a current focus is related to COVID-19: AI algorithms may contribute to early diagnosis. Among the available COVID-19 data, the blood test is a typical procedure performed when the patient seeks the hospital, and its use in the diagnosis allows reducing the need for other diagnostic tests that can impact the detection time and add to costs. In this work, we propose using self-organizing map (SOM) to discover attributes in blood test examinations that are relevant for COVID-19 diagnosis. We applied SOM and an entropy calculation in the definition of a hierarchical, semi-supervised and explainable model named TESOM (tree-based entropy-structured self-organizing maps), in which the main feature is enhancing the investigation of groups of cases with high levels of class overlap, as far as the diagnostic outcome is concerned. Framing the TESOM algorithm in the context of explainable artificial intelligence (XAI) makes it possible to explain the results to an expert in a simplified way. It is demonstrated in the paper that the use of the TESSOM algorithm to identify attributes of blood tests can help with the identification of COVID-19 cases. It providing a performance increase in 1.489% in multiple scenarios when analyzing 2207 cases from three hospitals in the state of Sao Paulo, Brazil. This work is a starting point for researchers to identify relevant attributes of blood tests for COVID-19 and to support the diagnosis of other diseases. T 202  
data, some of which focused on the frequency of positive cases in the city of Wuhan [5], recruitment of candidates for experimental treatments [6] and validation of effects of drugs for treatment in people diagnosed with COVID-19 [1; 7; 8]. To better define this data growth, the term _infodemic_ is being used. According to [9], it is a combination of the terms \"information\" and \"pandemic,\" serving as a reference for the excess of information (true or false) that hinders access to reliable sources of information and the definition of valid guidelines for decision making. Some studies were already carried out to help select reliable information, such as the use of epidemiological models to analyze data sources [10], where several cases of spread of misinformation were identified through the use of epidemiological models. There are several sources contributing to the current _infodemic_ related to COVID-19. Some examples come from Asiatic countries that are focused on pharmaceutical innovation [11], studies related to the macroeconomic crisis triggered by the COVID-19 pandemic [12], and also studies on the analysis of measures to help combat COVID-19 in the Russian Federation [13]. However, part of the proposed work on COVID-19 data is still based on algorithms whose focus is to improve the diagnostic prediction performance, without considering an understanding of the importance of variables and even the relationships between them in the diagnosis, making the decisions incomprehensible by specialists [14; 15; 16]. In operational terms, from a clinical point of view, it can be a problem not to give the doctor the opportunity to make the final decision [17]. Another aspect that should be highlighted, from the point of view of algorithmic limitations, is the so-called class imbalance [18]. In problems based on medical data, it is very common that more cases are related to one diagnostic outcome than to another [19].