Fig. 3.

Different strategies implemented in this study for model evaluation. In strategies 1–6, the data were split into 70%/30% train/test sets. In strategy 7, the centers were split into 70%/30% train/test sets. In strategy 8, the model was trained on one center and evaluated on 18 centers and repeated for all centers. In strategy 9, on each of the 19 iterations, 18 centers were used as the training set, and one as the external validation set. In strategy 10, the models were built and evaluated on each center separately using data splitting of 70%/30% train/test sets, respectively.