## 6 Evaluation of Created Models  
Both created classification models presented in Section 5.3 were finally evaluated (with respect to Formula (1)) on additional data sets. The characteristics of the first test data set were presented in Section 4. This data set was created as a result of splitting data into training and test parts (Table 3) and it consists of 577 examples. This data set will be referred to further as _PL_. The second data set was presented in Section 2 as the data set available online [24]. It required an initial transformation, which involved removing all patients who did not show any diagnostic symptoms used by the generated models and available in this collection (contact with infected person, number of days of symptoms, temperature > 38 \({}^{\circ}\)C, cough, dyspnoea, muscle aches, loss of smell or taste, sore throat, headache). The characteristics of the obtained data set are presented in Table 10. This data set will be referred to further as _US_. The results of applying the XGBoost classifier to the _PL_ test data set are presented in Tables 11 and 12. The final logistic regression model, presented in Table 6, was tested on the reduced _PL_ data set. In this set, 538 examples had full information for model features, which is required by the logistic regression classifier. The logistic regression model performance on this data set is presented in Tables 11 and 12. Additionally, the XGBoost classifier was applied to the reduced _PL_ test data set, in which there were no missing values. In this way, it was possible to compare the quality of both models. The results of XGBoost are presented in Tables 11 and 12. \begin{table}
\begin{tabular}{c c c} \hline \hline
**COVID-19(–)** & **COVID-19(+)** & **Total** \\ \hline
406 & 8 & 414 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Quantitative characteristics of the _US_ test data set. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**NPV** & **PPV** & **Sensitivity** & **Specificity** \\ \hline
0.952 & 0.509 & 0.968 & 0.402 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: XGBoost evaluation results—classification results for final model on the whole training set. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**XGBoost**} & \multicolumn{2}{c}{**Logistic Regression**} & \multicolumn{2}{c}{**XGBoost**} \\  & **Full Data Set** & **Reduced Data Set** & **Reduced Data Set** \\ \hline Sensitivity & 0.875 & 0.977 & 0.869 \\ Specificity & 0.316 & 0.113 & 0.324 \\ PPV & 0.374 & 0.347 & 0.384 \\ NPV & 0.844 & 0.911 & 0.836 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Confusion matrices obtained as a result of applying the generated models on the _US_ set (full and without missing values). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Reference**} \\ \cline{3-4}  & & \multicolumn{2}{c}{**COVID-19(\(-\))**} & **COVID-19(\(+\))** \\ \hline XGBoost (Full data set) & Predicted & COVID-19(\(-\)) & 124 & 23 \\  & & COVID-19(\(+\)) & 269 & 161 \\ \hline XGBoost (Reduced data set) & Predicted & COVID-19(\(-\)) & 117 & 23 \\  & & COVID-19(\(+\)) & 244 & 152 \\ \hline Logistic regression (Reduced data set) & Predicted & COVID-19(\(-\)) & 41 & 4 \\  & & COVID-19(\(+\)) & 322 & 171 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Classification quality of the logistic regression and XGBoost models on the _PL_ test data set. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**XGBoost**} & \multicolumn{2}{c}{**Logistic Regression**} & \multicolumn{2}{c}{**XGBoost**} \\  & **Full Data Set** & **Reduced Data Set** & **Reduced Data Set** \\ \hline Sensitivity & 0.625 & 0.833 & 0.667 \\ Specificity & 0.520 & 0.234 & 0.522 \\ PPV & 0.025 & 0.023 & 0.029 \\ NPV & 0.986 & 0.985 & 0.986 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Classification quality of the logistic regression and XGBoost models on the _US_ test data set.