Table 4.
Performance evaluation of RF and other machine learning algorithm
| Cross-validation | Algorithm | Performance evaluation parameters | |||||
|---|---|---|---|---|---|---|---|
| ACC(%) | SE(%) | SPE(%) | PPV (%) | NPV (%) | AUC | ||
| K2 | DT | 70.12 | 42.10 | 87.52 | 67.66 | 70.86 | 0.6712 |
| SVM | 70.95 | 44.36 | 87.46 | 68.75 | 71.66 | 0.6642 | |
| NB | 66.33 | 45.00 | 79.60 | 57.83 | 69.95 | 0.6874 | |
| RF | 71.03 | 34.88 | 91.01 | 70.88 | 69.20 | 0.7291 | |
| K5 | DT | 70.14 | 42.09 | 87.50 | 67.57 | 70.89 | 0.6788 |
| SVM | 70.96 | 44.46 | 87.46 | 68.77 | 71.71 | 0.6601 | |
| NB | 66.32 | 44.99 | 79.56 | 57.79 | 69.93 | 0.6885 | |
| RF | 71.96 | 35.35 | 91.03 | 71.45 | 69.31 | 0.7316 | |
| K10 | DT | 70.11 | 42.11 | 87.56 | 67.76 | 70.90 | 0.6686 |
| SVM | 70.94 | 44.45 | 87.43 | 68.78 | 71.68 | 0.6619 | |
| NB | 70.92 | 44.40 | 87.46 | 68.83 | 71.64 | 0.6774 | |
| RF | 71.37 | 33.53 | 91.63 | 71.34 | 68.91 | 0.7286 | |
Abbreviation: DT Decision tree, SVM Support Vector Machine, NB Naïve Bayes, RF Random forest, ACC Accuracy, SE Sensitivity, SPE Specificity, PPV Positive predictive value, NPV Negative predictive value, AUC Area under the curve, K2 Twofold cross-validation, K5 Fivefold cross-validation, K10 Tenfold cross-validation