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Table 2 Diagnostic performance of our method compared with 8 SOTA feature selection algorithms

From: Multi-parametric MRI phenotype with trustworthy machine learning for differentiating CNS demyelinating diseases

Category Method Roc_auc Sensitivity Specificity Accuracy
Filter Wilcoxon [28] 0.625 ± 0.120 0.604 ± 0.145 0.585 ± 0.137 0.593 ± 0.104
Filter Anova [29] 0.879 ± 0.037 0.883 ± 0.042 0.726 ± 0.067 0.789 ± 0.036
Filter mRMR [30] 0.846 ± 0.041 0.823 ± 0.091 0.754 ± 0.076 0.782 ± 0.050
Wrapper RF [31] 0.846 ± 0.041 0.828 ± 0.091 0.750 ± 0.075 0.781 ± 0.048
Wrapper SFS [32] 0.858 ± 0.038 0.782 ± 0.073 0.829 ± 0.121 0.810 ± 0.067
Embedded Lasso [33] 0.873 ± 0.050 0.884 ± 0.046 0.737 ± 0.087 0.796 ± 0.056
Embedded ElasticNet [34] 0.850 ± 0.064 0.868 ± 0.070 0.719 ± 0.135 0.779 ± 0.086
Embedded RFE [35] 0.814 ± 0.072 0.846 ± 0.058 0.612 ± 0.141 0.705 ± 0.090
Ours Clinical 0.573 ± 0.055 0.716 ± 0.190 0.446 ± 0.179 0.554 ± 0.070
Ours T2 MRI 0.852 ± 0.053 0.887 ± 0.067 0.733 ± 0.057 0.795 ± 0.045
Ours T1-MPR MRI 0.880 ± 0.033 0.798 ± 0.102 0.859 ± 0.073 0.835 ± 0.060
Ours Multi-parametric MRI 0.902 ± 0.027 0.873 ± 0.083 0.869 ± 0.051 0.871 ± 0.044
  1. The Bolded value indicates the highest value in each column
  2. AUC area under the curve, Anova analysis of variance, mRMR maximum relevance minimum redundancy, RF random forest, SFS sequential forward selection, Lasso least absolute shrinkage and selection operator, RFE recursive feature elimination