## 3 Proposed System Model
### Methods Used in the Machine Learning Phase  
The hyperparameters of all algorithms used in model training and their assigned values are summarized in Table 3. For each model, the dataset was split into 80% training and 20% test data, and all hyperparameter optimizations were performed using the GridSearch method. GridSearchCV aims to find the best result by systematically testing all combinations of hyperparameters, but it requires significant computational power. This method ensures that the model is optimized with the best parameters by evaluating all possibilities to achieve more accurate results [80]. While other optimization techniques such as RandomizedSearchCV, metaheuristics, and bio-inspired algorithms (e.g., genetic algorithms) could also be applied, GridSearchCV was selected for this study because of its systematic approach. Given the relatively limited and well-defined range of hyperparameters in this study, a more comprehensive search approach like GridSearchCV was deemed appropriate to thoroughly explore all potential parameter combinations. Additionally, the methods mentioned, such as RandomizedSearchCV, would provide faster results but might not guarantee the thorough evaluation of every possible combination, which is crucial for ensuring the most accurate model performance. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Algorithm**} & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** & **Combinations** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Logistic \\ Regression \\ \end{tabular} } & Parameter & C & class\_weight & \begin{tabular}{c} max\_ \\ iterations \\ \end{tabular} & Solver & Tolerance & & 40 \\ \cline{2-8}  & Value & 3 & Balanced & 5000 & liblinear & 0.0001 & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Naive \\ Bayes \\ \end{tabular} } & Parameter & Priors & \begin{tabular}{c} Var\_ \\ smoothing \\ \end{tabular} & & & & & 450 \\ \cline{2-8}  & Value & Auto & 20 & & & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Random \\ Forest \\ \end{tabular} } & Parameter & Criterion & n\_estimators & Max\_depth & \begin{tabular}{c} Min\_ \\ samples\_leaf \\ \end{tabular} & \begin{tabular}{c} Min\_ samples \\ split \\ \end{tabular} & \multirow{3}{*}{1600} \\ \cline{2-8}  & Value & Gini & 300 & 30 & 1 & 2 & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Decision \\ Trees \\ \end{tabular} } & Parameter & Criterion & Class\_weight & Max\_depth & \begin{tabular}{c} Min\_samples \\ leaf \\ \end{tabular} & \begin{tabular}{c} Min\_samples \\ split \\ \end{tabular} & Splitter & 640 \\ \cline{2-8}  & Value & Gini & Balanced & 30 & 1 & 2 & Best \\ \hline \multirow{3}{*}{\begin{tabular}{c} KNNs \\ \end{tabular} } & Parameter & Algorithm & Lead\_size & Metric & N\_neighbors & P & Weights \\ \cline{2-8}  & Value & Auto & 20 & Manhattan & 3 & 1 & Distance \\ \hline \multirow{3}{*}{SVM} & Parameter & C & Gamma & Kernel & Degree & & 56 \\ \cline{2-8}  & Value & 10000 & 10 & Rbf & 2 & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} XGBoost \\ \end{tabular} } & Parameter &
\begin{tabular}{c} Learning \\ rate \\ \end{tabular} & Max\_depth & N\_estimator & Subsample & Gamma & Reg\_alpha & 729 \\ \cline{2-8}  & Value & 0.08 & 7 & 130 & 0.9 & 0.15 & 0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameters of the machine learning algorithms used and the values determined by GridSearch. The cross-validation value used in model evaluation was set to 10. Cross-validation allows the model to be tested on different subsets of the data, providing a more reliable evaluation, and is generally used to improve the model's generalizability [81]. This systematic approach ensured that each algorithm worked with optimal settings and allowed for a fair comparison of model performances [82]. In the evaluation of the results, various metrics such as the F1 score, precision, recall, confusion matrix, ROC-AUC curve, and precision-recall curve were used to measure model performance.