The HT algorithm was especially efficient because it was able to deal with noisy data. The tree structure makes local decisions based on sufficient statistics, which reduced the impact of noise on the final classification performance. Cross-validation showed that this model performed with high accuracy. The AUROC indicates the robustness of the model since it can optimize the classification rate without creating any false positives. This shows the promise of HT-based model because it can distinguish which attributes are most important in miRNA-cancer association.