When modeling with filtered datasets (Table ), we used the same model architectures as for the complete dataset, incorporating all calculated features that had previously demonstrated the best performance. A comparison of model performance on the two subsets was conducted to evaluate the impact of rare and structurally distinct data on prediction quality. This approach helps determine whether filtering improves overall modeling outcomes by removing noisy data or, conversely, leads to the loss of important information needed for more accurate predictions.