Table 9 Comparison of prediction methods and results in our work and other studies
Attribute | [18] | [33] | [34] | Our approach |
|---|---|---|---|---|
Methods | Logistic regression, decision tree, random forest, k-nearest neighbors, support vector machine, naive Bayes | Random Forest, XGBoost, logistic regression, neural networks | Random Forest, naive Bayes, logistic regression | XGBoost with PCA for dimensionality reduction, SMOTE approach for data balancing |
Accuracy, % | 82 | 92.55 | - | 95 |
F1-score | 82.3 | 92.40 | 80 | 94.5 |