# Pervasive Intelligent Models to Predict the Outcome of COVID-19 Patients
## 5 Results and Discussion  
At this stage of the project, the obtained results in the modeling phase are exposed. The classification algorithm's best-obtained result and its respective scenario are shown for the target under study. In this way, it is possible to uncover which is the best model for the considered target. The prediction models were constructed considering the target under study (Patient Outcome), eight different scenarios (S1 to S8), and applied five different DM techniques (T1 to T5). For such, the models can be identified as an ensemble of a three-dimensional matrix \(M\) composed by \(s\) = 8 scenarios \(\times\)\(t\) = 5 techniques \(\times\)\(sm\) = 2 sampling methods. Each element of \(M\) corresponds to a particular model and can be defined as:  
\[M_{s,t,sm}=\left\{ \begin{array}{l l}s=1\,\ldots\,8\\ t=1\,\ldots\,4\\ sm=1,\,2\end{array} \right\}\]  
where,  
\(s\):  
\(1\) = [All comorbidities, symptoms, age and gender]  
\(2\) = [All comorbidities, age and gender]  
\(3\) = [Risk comorbidities, age and gender]  
\(4\) = [All comorbidities, symptoms and age]  
\(5\) = [All comorbidities and symptoms]  
\(6\) = [All comorbidities]  
\(7\) = [All comorbidities and age]  
\(8\) = [All comorbidities and gender]  
The table below presents the best results obtained by each scenario (\(s\) = 1 to 8). For each model, the values of Accuracy, Specificity, and Sensitivity are presented. For example, for model \(M_{1,\,3,\,1}\)--consisting of scenario 1, Decision Tree technique, and sampling method Oversampling--the Accuracy obtained was 90.99%, which represents the percentage of correctly labeled subjects. Specificity was 86.79%, indicating the percentage of recovered outcome correctly predicted, and Sensitivity was 95.14%, which represents the percentage of death outcome correctly identified. For example, taking into account the \(M_{1,\,3,\,1}\) model (composed by scenario 1--Decision Tree technique and sampling method Oversampling), the metrics Accuracy, Specificity, and Sensitivity were noted. The Accuracy obtained was 90.99%, representing the percentage of correctly labeled subjects; Specificity was 86.79%, which indicates the percentage of recovered outcome correctly predicted, and Sensitivity was 95.14%, representing the percentage of death outcome correctly identified. In this way, it is possible to understand the importance of the Sensitivity metric. It is preferable to obtain a forecast that indicates if the patient is going to die, but that, in reality, ends up recovering, than to predict the opposite, since it can affect the decisions of health professionals. All the results obtained can be consulted in the Supplementary Materials--\"Table S2--Classification Results\". Therefore, the best results obtained for Patient outcome, where bold is the metrics that achieved the threshold, shown in Table 7--Metrics for Patient Outcome, were as follows:As previously mentioned, the purpose of the project is to understand each patient's outcome, positive or negative, based on their characteristics. Therefore, after a brief analysis of the results presented, the main outputs gathered are discussed in this section. Firstly, it is important to remember the threshold previously defined to find the best model for the project, which is defined by the following metrics (in order of importance):  
* Sensitivity \(\geq\) 90%;
* Accuracy \(\geq\) 80%;
* Specificity \(\geq\) 80%. All values that managed to reach or exceed the defined threshold were marked in bold. That said, with the exception of models \(M_{6,\,3,\,1}\) and \(M_{8,\,3,\,2}\), all the other models can be considered for the prediction of the outcome of a given patient. In general, the models present interesting results for Sensitivity, and the lowest values are obtained by scenario 6 (all comorbidities) and scenario 7 (all comorbidities and age).