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Table 3 Evaluation of different combinations for feature selection and classifier validation on training set and test set

From: Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma

Dataset Classifier Feature selection AUC Accuracy Specificity Sensitivity
D1 ALD WRST 0.77 ± 0.08 0.81 ± 0.08 0.82 ± 0.05 0.67 ± 0.03
MRMR 0.67 ± 0.05 0.80 ± 0.04 0.84 ± 0.02 0.69 ± 0.09
RF 0.76 ± 0.03 0.79 ± 0.06 0.85 ± 0.02 0.62 ± 0.05
AQD WRST 0.81 ± 0.02 0.74 ± 0.09 0.80 ± 0.03 0.71 ± 0.08
MRMR 0.79 ± 0.06 0.77 ± 0.01 0.82 ± 0.01 0.73 ± 0.01
RF 0.72 ± 0.06 0.87 ± 0.02 0.86 ± 0.03 0.75 ± 0.03
RF WRST 0.83 ± 0.03 0.79 ± 0.06 0.82 ± 0.06 0.72 ± 0.04
MRMR 0.81 ± 0.06 0.76 ± 0.04 0.80 ± 0.08 0.70 ± 0.06
RF 0.80 ± 0.05 0.73 ± 0.08 0.79 ± 0.06 0.69 ± 0.08
SVM WRST 0.87 ± 0.03 0.89 ± 0.02 0.88 ± 0.01 0.78 ± 0.08
MRMR 0.84 ± 0.02 0.88 ± 0.01 0.84 ± 0.02 0.72 ± 0.04
RF 0.81 ± 0.05 0.84 ± 0.04 0.82 ± 0.02 0.73 ± 0.07
D2 SVM WRST 0.76 0.72 0.74 0.68
  1. Evaluation values in italic indicate the best machine learning combination
  2. MRMR minimum redundancy maximum relevance, RF random forest, WRST Wilcoxon rank sum test, LDA analysis of linear discriminant, AQD analysis of quadratic discriminant, SVM machine of support vector, AUC area under receiver operating curve