Fig. 10

Enhanced Computational Efficiency and Stable Accuracy Through Feature Selection in MBC-Associated ncRNA Classification. A Feature Importance Ranking Based on Information Gain—Critical Features (e.g., miR-21 Motifs) Exceeding the 0.05 Threshold. B Computational Efficiency Gains—A 42% Reduction in Training Time After Feature Selection (4,430 to 2,545 Features). C PCA Visualization of MBC-Associated ncRNAs—Clustering Post-Optimization with 82% Variance Explained (PC1 Correlated with TGF-β Signaling, ρ = 0.71). D Accuracy Retention During Feature Reduction—Consistent Model Accuracy (96.2%) with Decreased Feature Count from 4,430 to 2,545.