## 3 Results
### Machine Learning Models  
The hyperparameters of the machine learning models should be configured to tailor the machine learning models to the dataset. In most cases, the effects of hyperparameters on a model are well understood. The task of selecting the appropriate collection of hyperparameters and combinations of interacting hyperparameters for the dataset, on the other hand, is difficult. In this work, the grid search procedure is used to objectively explore multiple values for the machine learning model hyperparameters and choose a subset that results in a model that achieves the greatest performance on the dataset used. Grid search works by doing a full search across a specified subset of the training algorithm's hyperparameter space. Table 6 presents the optimal hyperparameters discovered using grid search for each model. The trained model was validated against the testing dataset using the best hyperparameters discovered via grid search. To make the comparison clearer, Table 7 shows the results obtained during training, whereas Table 8 shows the results of the trained model \begin{table}
\begin{tabular}{c l} \hline \hline
**Model** & \multicolumn{1}{c}{**Hyperparameters**} \\ \hline Logistic Regression & C: 2.0, penalty: I2, solver: liblinear \\ LogitBoost & C: 1.0 \\ MLP & activation: tanh, alpha: 0.0001, hidden\_layer\_sizes: (50), solver: adam \\ AdaBoost & learning\_rate: 1, n\_estimator: 200 \\ CatBoost & depth: 13, iterations: 200, learning\_rate: 0.1 \\ LightGBM & learning\_rate: 0.05, n\_estimators: 350, num\_leaves: 7 \\ XGBoost & learning\_rate: 0.1, max\_depth: 15, n\_estimators: 250 \\ Gradient Boosting & learning\_rate: 0.15, n\_estimators: 100 \\ kNN & n\_neighbors: 1 \\ Naïve Bayes & var\_smoothing: 2.84803586\(\times 10^{-6}\) \\ SVM & C: 0.1, gamma: scale, kernel: linear \\ Decision Tree & max\_depth: 13, min\_samples\_leaf: 3, min\_samples\_split: 2 \\ Random Forest & max\_depth: 25, min\_samples\_leaf: 1, n\_estimators: 55 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Optimal hyperparameters obtained for each model using grid search. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Original Dataset**} & \multicolumn{2}{c}{**SMOTE**} & \multicolumn{2}{c}{**SMOTE with Random**} \\  & & \multicolumn{2}{c}{**Undersampling**} & \multicolumn{2}{c}{**Testing**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} \\ \hline Accuracy & 0.7035 & 0.6626 & 0.7230 & 0.6626 & 0.7302 & 0.6748 \\ Precision & 0.5075 & 0.4545 & 0.7246 & 0.4563 & 0.7581 & 0.4700 \\ Sensitivity & 0.5667 & 0.6081 & 0.7194 & 0.6351 & 0.6763 & 0.6351 \\ Specificity & 0.7626 & 0.6860 & 0.7266 & 0.6744 & 0.7842 & 0.6919 \\ \(F1\)-score & 0.5354 & 0.5202 & 0.7220 & 0.5311 & 0.7148 & 0.5402 \\ Misclassification Rate & 0.2965 & 0.3374 & 0.2770 & 0.3374 & 0.2698 & 0.3252 \\ AUC & 0.6646 & 0.6471 & 0.7158 & 0.6548 & 0.7302 & 0.6635 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effects of using SMOTE and SMOTE with random sampling with a Logistic Regression model. using the testing dataset. According to these two tables, the models perform better during the training stage with 10-fold cross-validation than during the model testing stage. Because this work used a hold-out technique, the applicability of the model should be established by the findings obtained when the models were tested using the testing dataset. Figure 4 illustrates the performance of each machine learning model for each performance metric when the trained models were assessed using the testing dataset. The accuracy, precision, specificity, \(F1\)-score, misclassification rate, and AUC of LightGBM are the greatest. LightGBM, on the other hand, performs badly in the sensitivity test. Except for the sensitivity measure, Random Forest was the second-best model across all six performance metrics. On the other side, Decision Tree was the least performant machine learning model across all seven performance metrics.