Relevant features for the prediction of human papillomavirus status and overall survival
| Mean Variable Importance | Norm Hitsa/Coefficients | |
|---|---|---|
| Selected features by Boruta for prediction (Norm Hits)a | ||
| original_shape_Flatness | 4.753 | 0.812 |
| original_shape_SphericalDisproportion | 5.012 | 0.828 |
| wavelet_HLH_firstorder_Mean | 9.017 | 0.965 |
| wavelet_HLH_firstorder_Uniformity | 3.606 | 0.678 |
| wavelet_HLH_glcm_ClusterShade | 4.692 | 0.804 |
| wavelet_LHH_glcm_Idm | 3.296 | 0.600 |
| wavelet_LHH_glcm_Imc1 | 4.201 | 0.753 |
| wavelet_LHL_glszm_SmallAreaHighGrayLevelEmphasis | 11.378 | 0.989 |
| wavelet_LLH_glcm_Imc2 | 4.503 | 0.792 |
| Selected features by LASSO-Cox for OS (coefficient) | ||
| original_shape_SphericalDisproportion | 3.20E-01 | |
| original_firstorder_Minimum | –2.36E-03 | |
| original_firstorder_10Percentile | –1.75E–05 |
Note:—LASSO indicates least absolute shrinkage and selection operator; glcm, gray-level co-occurrence matrix; glszm, gray-level size-zone matrix; idm, inverse difference moment; imc1, informational measure of correlation 1; imc2, informational measure of correlation 2.
↵a Fraction of random forest runs (Norm Hits) in which they were more important than the most important shadow value.