## 3 Results
### Individual Features Analysis
#### 3.3.3 Presence of Consolidation  
A consolidation label equal to 1 (10% of the patients) identifies the patients with the presence of consolidation, while a label equal to 0 (90% of the patients) identifies its absence.  
The three best features selected by the Fisher criterion are (with type of feature indicated in parenthesis):  
* Cluster Prominence (Haralick);
* Median of the gray level distribution (Radiomics);
* Median of the elongation distribution (Radiomics).  
The three best features selected by the \(\chi^{2}\) criterion are (with type of feature indicated in parenthesis):  
* Median of the gray level distribution (Radiomics);
* Median of the elongation distribution (Radiomics);
* Cluster Prominence (Haralick).  
We show in Table 7 the results obtained in term of global adjusted accuracy and in Table 8 the precision, sensitivity, and F\({}_{1}\) score of the same prediction.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Crazy**} & & & & & \\ \multicolumn{2}{c}{**Paving**} & \multicolumn{2}{c}{**Fisher**} & & & & & \\ \multicolumn{2}{c}{**1 = Presence**} & \multicolumn{2}{c}{**PPV**} & \multicolumn{2}{c}{**\(\chi^{2}\)PPV**} & \multicolumn{2}{c}{**Fisher**} & \multicolumn{2}{c}{**\(\chi^{2}\)TPR**} & \multicolumn{2}{c}{**Fisher F\({}_{1}\)**} & \multicolumn{2}{c}{\(\chi^{2}\)F\({}_{1}\)**} \\  & \multicolumn{2}{c}{**0 = Absence**} & & & & & & \\ \hline Logistic & 0 & 0.56 & 0.60 & 0.49 & 0.57 & 0.52 & 0.58 \\  & 1 & **0.59** & **0.63** & **0.66** & 0.66 & **0.62** & **0.64** \\ Ridge & 0 & 0.48 & 0.55 & 0.41 & 0.46 & 0.44 & 0.50 \\  & 1 & 0.53 & 0.57 & 0.61 & 0.66 & 0.57 & 0.61 \\ KNN & 0 & 0.43 & 0.47 & 0.43 & 0.51 & 0.43 & 0.49 \\  & 1 & 0.49 & 0.43 & 0.49 & 0.49 & 0.49 & 0.51 \\ R. Forest & 0 & 0.48 & 0.50 & 0.35 & 0.35 & 0.41 & 0.41 \\  & 1 & 0.53 & 0.54 & **0.66** & **0.68** & 0.59 & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Precision, Sensitivity, and F1 Score for each model and each feature selection strat-egy to predict the presence of crazy paving. From the left: dichotomized score value, Pre-cision obtained using the 3 best features identified by the Fisher test, Precision obtained using the 3 best features identified by the \(\chi^{2}\) test; Sensitivity obtained using the 3 best features identified by the Fisher test, Sensitivity obtained using the 3 best features identified by the \(\chi^{2}\) test; F1 score obtained using the 3 best features identified by the Fisher test; F1 score obtained using the 3 best features identified by the \(\chi^{2}\) test. For each column the maximum value was indicated with bold font.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Fisher** & \(\chi^{2}\) & **All** \\ \hline Logistic & **0.57** & **0.61** & 0.47 \\ Ridge & 0.51 & 0.56 & 0.45 \\ KNN & 0.46 & 0.50 & **0.57** \\ R. Forest & 0.50 & 0.51 & 0.51 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Global adjusted accuracy of the models to predict the presence of crazy paving using different feature selection strategies. From the left: dichotomized score value, Precision obtained using the 3 best features identified by the Fisher test, Precision obtained using the 3 best features identified by the \(\chi^{2}\) test; Sensitivity obtained using the 3 best features identified by the Fisher test, Sensitivity obtained using the 3 best features identified by the \(\chi^{2}\) test; F1 score obtained using the 3 best features identified by the Fisher test; F1 score obtained using the 3 best features identified by the \(\chi^{2}\) test. For each column the maximum value was indicated with bold font.