• Dense Neural Network (DNN) with 2 hidden layers, batch normalization and dropout. Each DNN was trained for 100 epochs using AdamW as the optimizer and binary cross-entropy as the loss function on the training set. The parameters were optimized via Bayesian optimization for 20 iterations according to the ROC-AUC on the validation set. We tuned the number of units per layer (between 128 and 512 with a step of 128), the dropout rate (between 0 and 0.4), the learning rate (between 0.0001 and 0.05) and the batch size (between 16 and 64 with a step of 8). Once the optimal hyperparameters were determined on the validation set, we retrained on the training set and measured all metrics on the test set, repeating the procedure 5 times.