Skip to main content

Advertisement

Table 2 The highest values for accuracy, AUC, F-measure, precision, recall, sensitivity, and specificity for predicting responders vs. non-responders (A) and responders vs. relapsers (B) groups

From: Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms

A
  Subtype 1a (Responders vs. Non-Responders) Subtype 1b (Responders vs. Non-Responders)
Bayes Neural Networks SVM Decision Trees Bayes Neural Networks SVM Decision Trees
Database Chi Squared SVM Relief PCA SVM SVM Relief Gini Index
Algorithm Naive Bayes (Kernel) AutoMLp SVM DT Parallel Gini Index Naive Bayes (Kernel) AutoMLp SVM DT Random Forest Info Gain
Accuracy 74.17% 76.67% 74.17% 69.17% 89.17% 85.00% 75.00% 80.00%
AUC 0.84 0.68 0.75 0.59 0.94 0.94 0.84 0.83
AUC (optimistic) 0.84 0.68 0.75 0.83 0.94 0.94 0.84 0.85
AUC (pessimistic) 0.84 0.68 0.75 0.58 0.94 0.94 0.84 0.80
F-Measure 0.78 0.82 0.80 0.73 0.92 0.87 0.80 0.86
Precision 0.84 0.82 0.80 0.80 0.93 0.94 0.81 0.87
Recall 0.73 0.82 0.88 0.73 0.93 0.83 0.83 0.90
Sensitivity 0.73 0.82 0.88 0.73 0.93 0.83 0.83 0.90
Specificity 0.85 0.75 0.60 0.65 0.85 0.80 0.50 0.65
B
  Subtype 1a (Responders vs. Relapsers) Subtype 1b (Responders vs. Relapsers)
Bayes Neural Networks SVM Decision Trees Bayes Neural Networks SVM Decision Trees
Database Chi Squared SVM Relief PCA SVM SVM Relief Gini Index
Algorithm Naive Bayes (Kernel) AutoMLp SVM DT Parallel Gini Index Naive Bayes (Kernel) AutoMLp SVM DT Random Forest Info Gain
Accuracy 82.50% 79.17% 82.50% 81.67% 78.33% 78.33% 84.17% 81.67%
AUC 0.89 0.79 0.82 0.61   0.00   0.66
AUC (optimistic) 0.89 0.79 0.82 0.91   0.85   0.85
AUC (pessimistic) 0.89 0.79 0.82 0.74   0.15   0.47
F-Measure 0.84 0.86 0.86 0.84 0.87 0.87 0.91 0.89
Precision 0.92 0.83 0.90 0.90 0.78 0.78 0.84 0.82
Recall 0.82 0.92 0.87 0.80 1.00 1.00 1.00 1.00
Sensitivity 0.82 0.92 0.87 0.80 1.00 1.00 1.00 1.00
Specificity 0.85 0.55 0.75 0.85 0.00 0.00 0.29 0.14