## 3 Results
### Individual Features Analysis
#### 3.3.5 Peripheral GGO  
A peripheral GGO label equal to 1 (23% of the patients) identifies the patients with the presence of a peripheral GGO lesion, while a label equal to 0 (77% of the patients) identifies its absence.  
The three best features selected by the Fisher criterion are (with the type of feature indicated in parenthesis):  
* Patient age (Clinical);
* Minimum of the distance distribution (Radiomics);
* Skewness of the gray level distribution (Radiomics).  
The three best features selected by the \(\chi^{2}\) criterion are (with type of feature indicated in parenthesis):  
* Minimum of the distance distribution (Radiomics);
* Patient age (Clinical);
* Skewness of the elongation distribution (Radiomics).  
We show in Table 11 the results obtained in terms of global adjusted accuracy and in Table 12 the precision, sensitivity, and F\({}_{1}\) score of the same prediction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Roundish** & & & & & \\
**1 = Presence** & **Fisher** & & & & & \\
**0 = Absence** & **PPV** & \(\chi^{2}\)**PPV** & **Fisher TPR** & \(\chi^{2}\)**TPR** & **Fisher F\({}_{1}\)** & \(\chi^{2}\)**F\({}_{1}\)** \\ \hline Logistic & 0 & **0.92** & **0.92** & 0.64 & 0.64 & 0.75 & 0.75 \\  & 1 & 0.17 & 0.17 & 0.56 & 0.56 & 0.26 & 0.26 \\ Ridge & 0 & 0.91 & **0.92** & 0.61 & 0.64 & 0.73 & 0.75 \\  & 1 & 0.16 & 0.17 & 0.56 & 0.56 & 0.24 & 0.26 \\ KNN & 0 & 0.87 & 0.87 & **0.86** & **0.87** & 0.86 & 0.87 \\  & 1 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ R. Forest & 0 & 0.87 & 0.87 & 0.90 & 0.90 & **0.94** & **0.89** \\  & 1 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Precision, Sensitivity, and F\({}_{1}\) Score for each model and each feature selection strategy to predict the presence of roundish GGO lesions. 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 3 best features identified by the Fisher test, Sensitivity obtained using the 3 best features identified by the \(\chi^{2}\) test; F\({}_{1}\) score obtained using the 3 best features identified by the Fisher test; F\({}_{1}\) score obtained using the 3 best features identified by the \(\chi^{2}\) test. For each column the max-imum value was indicated with bold font.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Fisher** & \(\chi^{2}\) & **All** \\ \hline Logistic & **0.60** & **0.60** & **0.59** \\ Ridge & 0.58 & **0.60** & 0.56 \\ KNN & 0.43 & 0.43 & 0.50 \\ R. Forest & 0.45 & 0.45 & 0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Global adjusted accuracy of the models to predict the presence of roundish GGO 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 \(\chi^{2}\) test; F\({}_{1}\) score obtained using the 3 best features identified by the Fisher test; F\({}_{1}\) score obtained using the 3 best features identified by the \(\chi^{2}\) test. For each column the max-imum value was indicated with bold font.