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Table 4 Comparing the performance of CNFE-SE with other state of the art classifiers

From: CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features

Feature set Classifier Accuracy Sensitivity Specificity AUC F Score
All 296 features RF 0.58 ± 0.01 0.69 ± 0.05 0.46 ± 0.06 0.58 ± 0.01 0.55 ± 0.05
  DT 0.55 ± 0.01 0.62 ± 0.04 0.49 ± 0.04 0.55 ± 0.01 0.55 ± 0.04
  NB 0.53 ± 0.01 0.79 ± 0.11 0.26 ± 0.12 0.54 ± 0.01 0.39 ± 0.11
  ANN 0.50 ± 0.01 0.54 ± 0.16 0.45 ± 0.16 0.50 ± 0.01 0.49 ± 0.16
  SVM 0.54 ± 0.01 0.28 ± 0.1 0.8 ± 0.09 0.56 ± 0.01 0.41 ± 0.05
  XGboost 0.55 ± 0.01 0.53 ± 0.03 0.56 ± 0.03 0.55 ± 0.01 0.54 ± 0.03
  LGBM 0.60 ± 0.01 0.59 ± 0.03 0.59 ± 0.01 0.64 ± 0.01 0.59 ± 0.02
  Adaboost 0.59 ± 0.01 0.69 ± 0.02 0.48 ± 0.02 0.60 ± 0.01 0.56 ± 0.02
  CNFE-SE without FE 0.71 ± 0.01 0.69 ± 0.01 0.73 ± 0.01 0.71 ± 0.01 0.71 ± 0.01
  CNFE-SE with FE 0.85 ± 0.01 0.79 ± 0.01 0.91 ± 0.01 0.84 ± 0.01 0.85 ± 0.01
Only most important features RF 0.60 ± 0.02 0.69 ± 0.03 0.50 ± 0.02 0.59 ± 0.02 0.60 ± 0.02
  DT 0.57 ± 0.03 0.63 ± 0.01 0.54 ± 0.04 0.57 ± 0.02 0.58 ± 0.03
  NB 0.54 ± 0.01 0.52 ± 0.01 0.57 ± 0.01 0.54 ± 0.01 0.54 ± 0.01
  ANN 0.54 ± 0.01 0.55 ± 0.01 0.52 ± 0.01 0.53 ± 0.01 0.53 ± 0.01
  SVM 0.58 ± 0.01 0.51 ± 0.01 0.70 ± 0.01 0.60 ± 0.01 0.61 ± 0.01
  XGboost 0.58 ± 0.01 0.57 ± 0.01 0.59 ± 0.01 0.58 ± 0.02 0.58 ± 0.01
  LGBM 0.62 ± 0.02 0.61 ± 0.02 0.63 ± 0.03 0.62 ± 0.02 0.62 ± 0.02
  Adaboost 0.62 ± 0.01 0.69 ± 0.01 0.51 ± 0.01 0.61 ± 0.01 0.60 ± 0.01
  CNFE-SE without FE 0.72 ± 0.01 0.71 ± 0.01 0.74 ± 0.01 0.72 ± 0.01 0.72 ± 0.01
  CNFE-SE with FE 0.87 ± 0.01 0.82 ± 0.01 0.92 ± 0.01 0.87 ± 0.01 0.87 ± 0.01