## 3 Results  
The five algorithms analyzed in this study have been adjusted to determine their optimal combination of hyperparameters, as described in Table 2.  
Table 3 shows a comparison of the predictive performance of the five ML algorithms before optimization (using the default values of the corresponding software package) and after performing hyperparameter tuning. The results before hyperparameter optimization show that simpler models, such as LR and DT, outperformed more complex algorithms like XGB and DNN in various metrics. LR achieved the best precision and AUC (0.87 and 0.77, respectively), while GB exhibited the highest performance for recall and F1 score (0.88 and 0.87, respectively).  
After hyperparameter optimization, all algorithms demonstrated improved performance across every metric, except for LR, which exhibited a decrease in overall performance. The AUCs obtained before and after adjustment were 0.7 vs. 0.84 for XGB, 0.64 vs. 0.75 for DNN, 0.7 vs. 0.8 for GB, 0.62 vs. 0.7 for DT, and 0.77 vs. 0.72 for LR. This decrease in the results of LR may have happened because the optimal hyperparameters in each fold did not average to values that improve performance on the unseen test partition. This unexpected outcome could be attributed to collinearity issues, a phenomenon well known to regression models but less prevalent in other algorithms.  
Notably, the improvement in performance was substantial for XGB and DNN in all metrics. DNN showed substantial improvements over the results obtained using the predefined parameters, achieving up to a 20% increase in recall. For its part, XGB was the algorithm that achieved the highest performance among all, reaching precision = 0.92,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Optimized Hyperparameters** \\ \hline R & solver = ’saga’, penalty = 11’, \(C\) = 1, 11\_ratio = 0.1, class\_weight = None \\ \hline \multirow{3}{*}{DT} & criterion = ’gini’, splitter = ’best’, max\_depth = 3, min\_samples\_split = 2, \\  & min\_samples\_leaf = 1, max\_leaf\_nodes = 6, max\_features = None \\ \hline \multirow{3}{*}{GB} & min\_samples\_split = 0.1, min\_samples\_leaf = 1, max\_features = 10g2’, \\  & max\_leaf\_nodes = 8, learning\_rate = 0.05, n\_estimators = 16, max\_depth = 5 \\ \hline \multirow{3}{*}{XGB} & n\_estimators = 50, learning\_rate = 0.1, max\_depth = 4, \\  & min\_child\_weight = 1, subsample = 0.5, colsample\_bytree = 1, gamma = 0, \\  & scale\_pos\_weight = 1, reg\_alpha = 0, reg\_lambda = 1 \\ \hline \multirow{3}{*}{DNN} & number\_hidden\_layer = 1, epochs = 30, batch\_size = 64, dropout = 0.5, \\  & units = 100, kernel\_initializer = \"he\_normal’, activation = ’relu’, \\  & kernel\_constraint = 1, learning\_rate = 0.001, optimizer = ’Adam’ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal combination of hyperparameters for each algorithm.  
\begin{table}
\begin{tabular}{c|c c c c|c c c} \hline \hline  & \multicolumn{3}{c}{**Before HP Optimization**} & \multicolumn{3}{c}{**After HP Optimization**} \\ \hline  & **Precision** & **Recall** & **F1** & **AUC** & **Precision** & **Recall** & **F1** & **AUC** \\ \hline LR & **0.87** & 0.83 & 0.85 & **0.77** & 0.86 & 0.8 & 0.82 & 0.72 \\ DT & 0.83 & 0.78 & 0.8 & 0.62 & 0.87 & 0.86 & 0.86 & 0.7 \\ GB & 0.86 & **0.88** & **0.87** & 0.7 & 0.91 & 0.9 & 0.91 & 0.8 \\ XGB & 0.81 & 0.86 & 0.83 & 0.7 & **0.92** & **0.93** & **0.92** & **0.84** \\ DNN & 0.82 & 0.72 & 0.76 & 0.64 & 0.91 & 0.92 & 0.91 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of ML models before and after Hyperparameter (HP) Optimization.  
recall = 0.93, F1 = 0.92, and AUC = 0.84. These results suggest that algorithms with a higher number of hyperparameters, such as XGB and DNN, may derive greater benefits from hyperparameter optimization.