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Table 3 Cross validation and sample testing results for NHG

From: Artificial image objects for classification of breast cancer biomarkers with transcriptome sequencing data and convolutional neural network algorithms

  Accuracya AUCb Precision Recall F1 Score
Cross validation      
Grade I 0.838 ± 0.085 0.974 ± 0.005 0.913 ± 0.028 0.838 ± 0.085 0.871 ± 0.037
Grade II 0.809 ± 0.072 0.880 ± 0.012 0.686 ± 0.07 0.811 ± 0.072 0.738 ± 0.026
Grade III 0.825 ± 0.038 0.938 ± 0.004 0.863 ± 0.031 0.756 ± 0.072 0.803 ± 0.031
Weighted average 0.820 ± 0.012 0.931 ± 0.006 0.820 ± 0.012 0.802 ± 0.033 0.804 ± 0.030
Sample testing on GSE81538      
Grade I 0.406 ± 0.081 0.873 ± 0.025 0.608 ± 0.116 0.408 ± 0.082 0.475 ± 0.059
Grade II 0.743 ± 0.069 0.833 ± 0.005 0.710 ± 0.026 0.745 ± 0.070 0.725 ± 0.026
Grade III 0.872 ± 0.029 0.928 ± 0.015 0.848 ± 0.022 0.873 ± 0.030 0.858 ± 0.015
Weighted average 0.764 ± 0.052 0.882 ± 0.012 0.762 ± 0.035 0.765 ± 0.052 0.758 ± 0.025
Sample testing on GSE163882      
Grade I 0 ± 0 0.622 ± 0.189 0 ± 0 0 ± 0 0 ± 0
Grade II 0.016 ± 0.006 0.564 ± 0.044 0.268 ± 0.133 0.016 ± 0.006 0.030 ± 0.010
Grade III 0.974 ± 0.012 0.596 ± 0.070 0.589 ± 0.003 0.974 ± 0.012 0.734 ± 0.004
Weighted average 0.580 ± 0.006 0.587 ± 0.038 0.437 ± 0.045 0.330 ± 0.003 0.443 ± 0.004
  1. aCategorical accuracy, bclass-specific AUC