Skip to main content

Table 5 Comparison of results of different classifier models on the same data set

From: An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network

Classifier ACC (%) SEN (%) SPE (%) PRE (%) MCC (%) AUC (%)
Adaboost 70.82 ± 0.35 71.30 ± 1.15 70.34 ± 0.88 70.62 ± 0.41 41.65 ± 0.71 78.05 ± 0.52
Logistic 72.95 ± 0.45 72.98 ± 0.99 72.92 ± 0.68 72.94 ± 0.44 45.91 ± 0.91 80.41 ± 0.54
Naïve Bayes 68.27 ± 0.55 70.86 ± 0.86 65.69 ± 0.76 67.37 ± 0.53 36.60 ± 1.10 74.18 ± 0.62
Random forest 79.84 ± 0.50 80.03 ± 0.95 79.64 ± 0.22 79.72 ± 0.28 59.68 ± 1.00 87.90 ± 0.54