Table 3 Metrics of model accuracy for each classifier machine-learning model as assessed using test data.
Machine learning algorithms | Â | ||||||
|---|---|---|---|---|---|---|---|
|  | Decision Tree (%) | Random Forest (%) | Naïve Bayes (%) | Logistic Regression (%) | SVM (%) | Gradient Boosting (%) | KNN (%) |
Sensitivity | 27.91 | 41.86 | 0.00 | 13.95 | 4.65 | 30.23 | 37.21 |
Specificity | 97.74 | 99.25 | 98.50 | 96.24 | 100 | 95.49 | 92.48 |
PP Value | 80.00 | 94.74 | 0.00 | 54.55 | 100 | 68.42 | 61.54 |
NP Value | 80.75 | 84.08 | 75.29 | 77.58 | 76.44 | 80.89 | 82.00 |
Accuracy | 80.68 | 85.23 | 74.43 | 76.14 | 76.70 | 79.55 | 78.98 |
AUC | 73.90 | 73.10 | 72.2 | 73.80 | 75.10 | 81.40 | 72.50 |
