Classification model selection
To compare our classification models, optimized and trained with different sets of metrics and different segmentations, we considered their global Area Under the Curve (AUC), of the Receiver-Operating Characteristic (ROC) curve, computed by the PhotonAI toolbox, and summarizing the sensibility and specificity of a binary classification model. As a direct implication of the classification procedure, each experiment resulted in 60 AUC values. To evaluate our models, we eventually considered the mean and standard deviation of these 60 AUC values. Significance of the difference of AUC distributions was assessed with non-parametric two-sided Mann–Whitney tests. After the training, we evaluated the importance of each feature of the best model, using the Mean Decrease impurity, in order to identify the key metrics that influence the model prediction.