To ensure the model's robustness and ability to generalize accurately to new data, we performed 10-fold cross-validation on the training set, constituting 10% of the dataset. Data was randomly partitioned in each fold into 10 subsets, with 9 subsets utilized for training and the remaining subset for validation. This procedure was repeated 10 times.