Performance of the proposed model with Gaussian filtering across five cross-validation folds (I-V). Metrics include Accuracy, Balanced Accuracy, Recall (TPR), Specificity (TNR), Precision (PPV), NPV, F1-Score, and Cohen’s Kappa, with averages in the final column. Compared to the baseline (Table 4), the model achieves higher average accuracy (77.3 % vs. 72.4 %) and balanced accuracy (82.5 % vs. 76.9 %), while maintaining very high specificity (97.4 %). This indicates overall improved classification performance and better handling of class imbalance.
收藏NIAID Data Ecosystem2026-05-10 收录
下载链接：
https://figshare.com/articles/dataset/Performance_of_the_proposed_model_with_Gaussian_filtering_across_five_cross-validation_folds_I-V_Metrics_include_Accuracy_Balanced_Accuracy_Recall_TPR_Specificity_TNR_Precision_PPV_NPV_F1-Score_and_Cohen_s_Kappa_with_averages_in_the_final_c/30558793
下载链接
链接失效反馈官方服务：
资源简介：
Performance of the proposed model with Gaussian filtering across five cross-validation folds (I-V). Metrics include Accuracy, Balanced Accuracy, Recall (TPR), Specificity (TNR), Precision (PPV), NPV, F1-Score, and Cohen’s Kappa, with averages in the final column. Compared to the baseline (Table 4), the model achieves higher average accuracy (77.3 % vs. 72.4 %) and balanced accuracy (82.5 % vs. 76.9 %), while maintaining very high specificity (97.4 %). This indicates overall improved classification performance and better handling of class imbalance.
创建时间：
2025-11-06
