## 2 Related Literature Review
### Review for Supervised Machine Learning Used to Predict and Classify School Dropout Rates
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No** & **Author** & **Title** & **Algorithm Used** & **Chosen Algorithm** & **Result** \\ \hline \multirow{3}{*}{1.} & \multirow{3}{*}{[21]} & The Use of & \multirow{3}{*}{Neural networks (NNs), decision tree (DT), logistic regression (LR), support vector machine (SVM)} & Logistic regression (LR) & Accuracy: 84.8 \\  & & & & \\  & & & & \\ \hline \multirow{3}{*}{2.} & \multirow{3}{*}{[23]} & Perspectives to Predict & Neural networks (NNs), & \multirow{3}{*}{Random forest (RF)} & \multirow{3}{*}{Accuracy: 91\%} \\  & & Dropout in University & random forest (RF), logistic regression (LR), support machine (SVM) & & \\  & & & & \\ \hline \multirow{3}{*}{3.} & \multirow{3}{*}{[24]} & Using Machine Learning & \multirow{3}{*}{Multilayer perceptron, decision tree (DT), random forest (RF), support vector machine (SVM)} & Random forest (RF) & Accuracy: 70.98 \\  & & & & \\ \hline \multirow{3}{*}{4.} & \multirow{3}{*}{[25]} & Predicting of Student & \multirow{3}{*}{Artificial neural network (ANN), decision tree (DT), Bayesian networks (BNs)} & \multirow{3}{*}{Decision tree (DT)} & \multirow{3}{*}{Accuracy: 63.89\%} \\  & & Program Through the Use of & & & \\ \hline \multirow{3}{*}{5.} & \multirow{3}{*}{[19]} & The Machine Learning-Based & \multirow{3}{*}{Random forest (RF), boosted decision tree (BDT), random forest with SMOTE (SMOTE + RF), boosted decision tree with SMOTE (SMOTE + BDT)} & Boosted decision tree (BDT) & Accuracy: 89.8\% \\  & & & & \\ \hline \multirow{3}{*}{6.} & \multirow{3}{*}{[26]} & Towards Predicting Student’s & \multirow{3}{*}{Random forest, logistic regression, neural network} & \multirow{3}{*}{Random forest} & \multirow{3}{*}{Accuracy: 95.00\%} \\  & & Dropout in Higher Education & & & \\ \cline{1-1}  & & Using Supervised Machine & \multirow{3}{*}{Random forest} & \multirow{3}{*}{Accuracy: 95.00\%} \\  & & Learning Techniques & & & \\ \hline \multirow{3}{*}{7.} & \multirow{3}{*}{[27]} & Supervised Machine Learning & \multirow{3}{*}{Decision tree, SVM, random forest, gradient boosting, XGBoost, CatBoost, LightGBM} & \multirow{3}{*}{LightGBM, CatBoost} & \multirow{3}{*}{F1 score: 86\%} \\  & & Algorithms for Predicting & \multirow{3}{*}{Logistic regression, decision tree, random forest, XGBoost} & & & \\ \cline{1-1}  & & Student Dropout Prediction & & & \\ \cline{1-1}  & & for University with High & \multirow{3}{*}{XGBoost} & \multirow{3}{*}{XGBoost} & The XGBoost model achieved \\ \cline{1-1}  & & Precision and Recall & & & \\ \hline \multirow{3}{*}{9.} & \multirow{3}{*}{[29]} & Development of Prediction & \multirow{3}{*}{Logistic regression, decision tree, random forest} & \multirow{3}{*}{Random forest} & The random forest model (87.4\%) in predicting student dropout in a cyber university setting \\ \cline{1-1}  & & Dropout Prediction in & & & \\ \cline{1-1}  & & MOOCs: Using Deep & \multirow{3}{*}{Deep neural network} & \multirow{3}{*}{Deep neural network} & The proposed deep learning model achieved an AUC of 0.82 in predicting MOOOT, dropout, outperforming \\ \cline{1-1}  & & Intervention & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related literature review for the application of supervised machine learning algorithms in the context of school dropout prediction and classification.