## 6 Conclusions and Future Works  
With the increasing volume of information generated every day in health centers and hospitals, it is now a demand to use artificial intelligence (AI) techniques to assist in clinical diagnosis and identification of predictive factors of poor or good prognosis, associations with outcomes and comorbidities and death. In laboratory tests, the risk of loss of sensitivity or the generation of a false diagnosis impacting the outcome of the patient has led specialists to become interested in algorithms whose results can also be interpreted by a human. In this sense, AI is a tool for exploring algorithms with the aim of improving \begin{table}
\begin{tabular}{l c} \hline
**Attribute** & **Relevance \%** \\ \hline Monocytes.Absoluto & 100.000 \\ Platelets & 99.990 \\ Leukocytes & 91.247 \\ Lymphocytes.Absoluto & 86.264 \\ Red.blood.Cells & 75.683 \\ MCHC & 73.519 \\ Hematocrit & 72.402 \\ MCV & 67.696 \\ MCH & 65.961 \\ Hemoglobin & 58.374 \\ RDW & 58.342 \\ Eosinophils.Absoluto & 51.907 \\ Basophils.Absoluto & 0.000 \\ \hline \end{tabular}
\end{table} 
Table 15: Relevance of variables in the **random forest** training, for comparative purposes. \begin{table}
\begin{tabular}{l c c c} \hline
**Variable** & **Neuron 4** & **Neuron 3.2** & **Neuron 2.2.4** \\ \hline Leukocytes & & & \\ Basophils.Absoluto & & & X \\ RDW & & & \\ Eosinophils.Absoluto & & & \\ Hematocrit & X & & \\ Hemoglobin & X & & \\ Platelets & X & X & X \\ Red.blood.Cells & X & & X \\ Lymphocytes.Absoluto & & X & X \\ MCHC & X & X & \\ MCH & X & & X \\ MCV & & & X \\ Monocytes.Absoluto & & & X \\ \hline \end{tabular}
\end{table} 
Table 14: Summary of the variables found in the neuron analysis—**TESSOM**. diagnostic recognition rates. Thus, identifying variables that contribute to decision making in clinical diagnoses and allow an understanding of how the algorithm reaches a decision may assist with the search for new treatments and early identification of positive cases until diseases are adequately treated. In this sense, this paper presented an algorithm that uses the self-organizing map as a base, extending its characteristic of analyzing similarity in data and topological maintenance of characteristics to a hierarchical structure in the form of a tree, which makes it possible to delve into data whose diagnosis is not precise. The **TESSOM** algorithm allowed analyzing positive cases of COVID-19 from blood test data, serving as an auxiliary tool in identifying variables in the blood count that most impact the algorithmic discrimination between positives and negatives for COVID-19. Thus, in addition to enabling decision support in cases of imprecise diagnosis, it is also possible to identify, for each variable, the range of actual values shown in positive cases, supporting the exploration of visual correlations and aiding in disease monitoring. To validate the experiments and demonstrate the refinement and robustness of our method, we presented statistical analyses to verify the significance of **TESSOM**'s results. The inclusion of **XAI** characteristics in **TESSOM** extends results from classic algorithms in the literature, such as **random forest**, which presents only the hierarchy of relevant variables, without relating them in a combination of factors that influence the diagnosis. As future work, we intended to consolidate the results into a single tool to facilitate user interpretation in terms of usability. Parametric sensitivity analysis, with a theoretical rationale, of **TESSOM**, is also a work to be done in the future, although the results obtained are statistically similar to those of other algorithms. Studies using TESSOM will also be performed using other information, such as socio-economical and cultural distribution, and geographical data, in order to identify variations related to these attributes.