## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Author(s) \& Year** & \
\begin{tabular}{c} **Feature Selection (FS),** \\ **Classifiers Trained, \&** \\ **Validation Method** \\ \end{tabular} &
\begin{tabular}{c} **Performance Metrics \&** \\ **Software (SW)** \\ \end{tabular} & **Remarks** \\ \hline
1. & \begin{tabular}{c} [PERSON] et al. [31] \\ 2019 \\ \end{tabular} & \begin{tabular}{c} FS: Random search algorithm \\ Classifiers: RF \\ Hold-out (70:30) validation \\ \end{tabular} & \
\begin{tabular}{c} Accuracy, sensitivity, specificity, \\ MCC, and ROC-AUC \\ SW: Python programming \\ \end{tabular} &
\begin{tabular}{c} Only one classifier has been \\ trained and the dataset samples \\ become fewer when using \\ hold-out validation. \\ \end{tabular} \\ \hline
2. & \begin{tabular}{c} [PERSON] et al. [32], 2019 \\ \end{tabular} & \begin{tabular}{c} FS: InfoGain, GainRatio, \\ Correlation, OneR, and Relief \\ Classifiers: RF \\ 10-fold cross-validation (CV) \\ \end{tabular} & \
\begin{tabular}{c} Accuracy and AUC \\ SW: Weka \\ \end{tabular} &
\begin{tabular}{c} One classifier has been utilized \\ and the accuracy achieved was \\ a moderate value. \\ \end{tabular} \\ \hline
3. & \begin{tabular}{c} [PERSON] et al. [33], \\ 2019 \\ \end{tabular} & \begin{tabular}{c} FS: Parasitism-predation \\ algorithm \\ Classifiers: KNN with 10-fold \\ CV \\ \end{tabular} & \
\begin{tabular}{c} Accuracy and execution time \\ SW: Matlab 2017a \\ \end{tabular} &
\begin{tabular}{c} Only one classifier has been \\ trained and achieved moderate \\ accuracy. \\ \end{tabular} \\ \hline
4. & \begin{tabular}{c} [PERSON] et al. [34], \\ 2020 \\ \end{tabular} & \begin{tabular}{c} FS: Novel feature reduction \\ Classifiers: LR, RF, BRT, SGB, and SVM \\ Hold-out (70:30) validation \\ \end{tabular} & \
\begin{tabular}{c} Accuracy and AUC \\ SW: R programming \\ \end{tabular} &
\begin{tabular}{c} Dataset samples were lesser \\ with the 70:30 train-test split \\ resulting in high variance. \\ \end{tabular} \\ \hline
5. & \begin{tabular}{c} [PERSON] et al. [35], \\ 2019 \\ \end{tabular} & \begin{tabular}{c} FS: MFSFA, FFSA, RFSA \\ Classifiers: RBF SVM with \\ 10-fold CV \\ \end{tabular} & \
\begin{tabular}{c} Accuracy, sensitivity, and \\ specificity \\ SW: not specified \\ \end{tabular} &
\begin{tabular}{c} Only one classifier has been \\ trained and the accuracy \\ achieved was poor. \\ \end{tabular} \\ \hline
6. & \begin{tabular}{c} [PERSON] et al. [36], \\ 2021 \\ \end{tabular} & \begin{tabular}{c} FS: LASSO \\ Classifiers: LR, KNN, SVM, RF, \\ DT, and DL with 10-fold \\ cross-validation \\ \end{tabular} & \
\begin{tabular}{c} Accuracy, specificity, and \\ sensitivity \\ SW: Python programming \\ \end{tabular} &
\begin{tabular}{c} Achieved good accuracy of \\ 94.2\%. However, the specificity \\ and sensitivity are poor. \\ \end{tabular} \\ \hline
7. & \begin{tabular}{c} [PERSON] et al. \\ [37], 2020 \\ \end{tabular} & \begin{tabular}{c} FS: FCBF, mRMR, LASSO, \\ and Relief \\ Classifiers: KNN, DT, Extra-Tree (ET), RF, LR, NB, ANN, SVM, AdaBoost, and GB with 10-fold cross-validation \\ \end{tabular} & \
\begin{tabular}{c} Accuracy, Sensitivity, Specificity, \\ AUC, Precision, F1-Score, MCC \\ SW: Python programming \\ \end{tabular} &
\begin{tabular}{c} ET classifier provided 92.09\% of \\ accuracy with full features, and \\ 94.41\% with Relief FS. \\ \end{tabular} \\ \hline
8. & \begin{tabular}{c} [PERSON] et al. [38], 2019 \\ \end{tabular} & \begin{tabular}{c} FS: Chi-square \\ Classifiers: Optimized deep \\ neural network \\ \end{tabular} & \
\begin{tabular}{c} Accuracy, specificity, sensitivity, \\ MCC, and AUC \\ SW: Python programming \\ \end{tabular} &
\begin{tabular}{c} Though sensitivity is 100\%, the \\ accuracy is about 7\% less \\ than that. \\ \end{tabular} \\ \hline
9. & \begin{tabular}{c} [PERSON] \\ et al. [39], 2022 \\ \end{tabular} & \begin{tabular}{c} FS: Pearson’s correlation \\ coefficient \\ Classifiers: NB, XGBost, k-NN, \\ SVM, MLP, and CatBoost \\ 10-fold cross-validation, 80:20 \\ train-test \\ \end{tabular} & \
\begin{tabular}{c} Accuracy and AUC \\ SW: Python programming \\ \end{tabular} &
\begin{tabular}{c} With the CatBoost classifier \\ achieved 94.34\% of accuracy, \\ whereas other classifiers \\ underperformed. \\ \end{tabular} \\ \hline
10. & \begin{tabular}{c} [PERSON] et al. [40], \\ 2022 \\ \end{tabular} & \begin{tabular}{c} Classifiers: LR, SVM, NB, DT, \\ KNN, and RF \\ 70:30 train-test split \\ \end{tabular} & \
\begin{tabular}{c} Accuracy, sensitivity, specificity, \\ precision, and F1 score \\ SW: Python programming \\ \end{tabular} &
\begin{tabular}{c} No feature selection method \\ was employed. Lesser number \\ of observations for validating \\ the model. \\ \end{tabular} \\ \hline
11. & \begin{tabular}{c} [PERSON] et al. [41], \\ 2022 \\ \end{tabular} & \begin{tabular}{c} Classifiers: AB, LR, ET, MNB, \\ CART, SVM, LDA, RF, and \\ XGB.10-fold cross-validation \\ \end{tabular} & \
\begin{tabular}{c} Accuracy, precision, recall, and \\ F measure \\ SW: Python programming \\ \end{tabular} &
\begin{tabular}{c} Achieved better accuracy value \\ by performing hyperparameter \\ tuning but no feature selection \\ technique has been applied. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}