## 4 Discussion
### Individual Features Analysis  
We noticed an evident improvement in the prediction converting the clinical outcomes to dichotomized classes. A second improvement in the quality of the prediction was obtained by the application of the feature reduction techniques, which allow a stabilization of the results. Most of the predictive power for each feature can be synthesized in 3 to 4 features per variable. Of all the variables, particular prominence was observed for the Radiomic features. These features were the most important ones in most of the predictions. Of all the predicted characteristics and outcomes, only the peripherality of GGO and PREDI-CO score requires the inclusion of the age value. For the PREDI-CO score, this is not unexpected as it is one of the components of this score. As for the peripherality of GGO lesions, this is an interesting result as they are an important predictor of clinical outcomes and not intuitively associated with patients' age. If one considers the results for the different predictors (linear penalized, KNN, Random Forest), one can observe that in general, KNN and Random Forests achieve similar performances, while the penalized linear methods consistently perform better. This can be interpreted as the effect of a progressive non-dichotomic behavior in the system. These linear models were also the ones that gained the least from the pre-selection of the features. This can be explained as the L1 and L2 penalization already reducing the effect of the numerosity of the provided features. Methods such as KNN, based on features metrics, are particularly affected by the features numerosity and thus are the ones that have the greatest improvement by feature selection. Linear penalized methods, on the other end, include an implicit feature selection internally and could be even penalized by a reduction in the number of considered features. Most of these features have a strong class unbalance (down to around 10% of samples in one group, such as in the Consolidation and Roundish GGO), and therefore, the prediction score tends to be strongly unbalanced, with a strong penalization for the prediction of the least represented class. When this unbalance is not present, such as in the case of the prediction of the PREDI-CO score, one can observe good, balanced prediction scores. The prediction scores for the PREDI-CO are also higher than for similarly balanced classes (such as Crazy Paving). This indicates that the extracted features, albeit not optimal for predicting individuals' components of the score, are indeed able to predict the score as a global value. It is interesting to notice that the variables considered as the most important in the prediction (both for the Fisher and \(\chi^{2}\) method) alongside the age (a well-known risk factor) are (1) the distance between the GGO area and the trachea and (2) the irregularity of the GGO lesion shape. This follows the clinical hypothesis that the spreading of the damaged area toward the peripheral area of the lungs leads to the worst prognosis for the patient. The results obtained in this work cannot overcome the performances of the already published artificial intelligence techniques. The main limitation of this work is related to the number of available samples: semi-supervised learning algorithms are designed to work with small datasets but require better labeling of them compared to supervised methods. A second criticality is given by the preliminary choice of the number of clusters for the k-means algorithm: in our work, we identified only five putative clusters for the tissue segmentation. This degree of freedom determines the quality of the areas used for the radiomic feature extraction, and therefore, it could affect the efficiency of the prediction models. In contrast, the manipulation of this single degree of freedom could help to achieve better results on in-patient segmentations.