## 4 Results and Discussion
### KNN Results  
The first model applied was K-Nearest Neighbor (KNN). For this method, analyses were performed, as shown in Table 5. The result obtained using three different K values, (5, 25, and 45) is observed. The distance validation metrics were analysed for each K value: Euclidean, Manhattan, and Hamming.  
The value of K equal to 45, as shown in Figure 4, using the Hamming distance measure, was the configuration with the best overall performance (accuracy), obtaining 75.27% confidence in the estimate. Its execution time was 69.995 s, the fastest execution using Euclidean distance and K equal to 5, taking 7.132 s, but it was the least accurate among the analysed K values.  
An accuracy of 74.93% was obtained according to the parameters, indicating the percentage of correct classification of clinical evolution. Recall (sensitivity), which indicates the frequency of the correct classification, obtained a value of 0.9322. KNN presents a good frequency of assertiveness (optimal value is equal to 1) when classifying the evolution of the clinical case as high: hospital or death. When combining the values obtained from precision and recall, we have an F1 score of 0.83082 (optimal value equal to 1). The value reached corroborates the information on the precision and recall values. AUC was performed for all K values, and the respective distances were investigated. Figure 5 shows the graph generated through the best parameters identified, reaching a value of 0.76 (ROC score). With this information, a patient chosen randomly is evaluated, with 76% assertiveness in classifying their clinical evolution, using the K-Nearest Neighbor (KNN) prediction model.  
Figure 4: K optimized.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{**K-Nearest Neighbor–KNN**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \multicolumn{1}{c}{**Metrics/ Distance KNN**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{_Recall_} & \multicolumn{1}{c}{_F1_} & \multicolumn{1}{c}{_Score_} & \multicolumn{1}{c}{_Recall_} & \multicolumn{1}{c}{_That_} & \multicolumn{1}{c}{_Score_} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{_Recall_} & \multicolumn{1}{c}{_F1_} \\ \hline \multicolumn{1}{c}{**Neighbor K**} & \multicolumn{1}{c}{**5**} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \multicolumn{1}{c}{_Euclidean_} & 71.17\% & 74.31\% & 0.8518 & 0.79380 & 73.47\% & 74.25\% & 0.9073 & 0.81671 & 74.45\% & 75.04\% & 9107 & 0.82283 \\ \hline \multicolumn{1}{c}{_Manhattan_} & 71.17\% & 74.32\% & 0.8516 & 0.79375 & 74.07\% & 74.62\% & 0.9122 & 0.82090 & 74.87\% & 75.09\% & 0.9191 & 0.82654 \\ \hline \multicolumn{1}{c}{_Hamming_} & 71.87\% & 74.61\% & 0.8613 & 0.79957 & 74.65\% & 74.71\% & 0.9235 & 0.82599 & **75.27\%** & **74.93\%** & **0.9322** & **0.83082** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Benchmark for K-Nearest Neighbor–KNN.