## 7 Conclusions  
The main objective of this work is to improve the survival classification of hepatocellular carcinoma, with minimal features. Initially, missing values are replaced using mean and mode, and k-NN is performed. Then, different feature-selection methods using the wrapper and embedded methods are applied. The wrapper method is based on the RFE with GB and random forests. The embedded method is based on logistic regression using LASSO and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**S.No** & **Method** & **Accuracy** & **F1-Score** & **Reference** &
\begin{tabular}{c} **Year of** \\ **Publication** \\ \end{tabular} \\ \hline
1 & NN + augmented set & 75.19\% & 66.50\% &
\begin{tabular}{c} [PERSON] et al. \\ [36] \\ \end{tabular} & 2017 \\
2 & BFA + RF & 83.5\% & - &
\begin{tabular}{c} [PERSON] \\ et al. \\ [37] \\ \end{tabular} & 2018 \\
3 & SVC with GA & 88.49\% & 87.62\% &
\begin{tabular}{c} [PERSON] \\ [40] \\ \end{tabular} & 2019 \\
4 & LDA-GA-SVM & 90.30\% & - & [PERSON] et al. [41] & 2019 \\
5 & GA & 90.30\% & 88.57\% & \
\begin{tabular}{c} [PERSON] \\ [42] \\ \end{tabular} & 2020 \\  & LASSO + SVM & & &
\begin{tabular}{c} [PERSON] \\ [PERSON] \\ \end{tabular} & 2019 \\
6 & RFE + LASSO + SVM & 89.18\% & - &
\begin{tabular}{c} [PERSON] et al. \\ [40] \\ \end{tabular} & 2020 \\
7 & K-means + SMOTE + SVM & 84.90\% & - &
\begin{tabular}{c} [PERSON] et al. \\ [43] \\ \end{tabular} & 2020 \\
8 & Relief + LDA & 92.12\% & 91.61\% &
\begin{tabular}{c} Al-Islam \\ [44] \\ \end{tabular} & 2020 \\
9 & SMOTE + XGBOOST & 87\% & - &
\begin{tabular}{c} [PERSON]- \\ [PERSON] et al. \\ [45] \\ \end{tabular} & 2021 \\
10 & GA-LR & 94.55\% & 93.56\% &
\begin{tabular}{c} [PERSON], \\ [PERSON] et al. \\ [46] \\ \end{tabular} & 2021 \\
11 & NCA + GA + SVM & 87.4\% & - &
\begin{tabular}{c} [PERSON] \\ [47] \\ \end{tabular} & 2022 \\
12 & RFE-GB-RF & 96.66\% & 95.66\% &
\begin{tabular}{c} This study \\ \end{tabular} & 2022 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the present study with other state-of-the-art methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Imputation** & **LR-L1** & **LR-L2** & **RFE-RF** \\ \hline Mean and Mode & 0.008 & 0.018 & 0.013 \\ \hline k-NN & 0.006 & 0.014 & 0.010 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(p\)-values of Wilcoxon test of the methods. ridge regression. Experimental results show that the embedded method using the RFE-GB estimator gives the most accurate results, compared to other feature-selection methods, with accuracy and F1-score values of 96.66% and 95.66%, respectively. Future work would be to identify different cancer variants using deep learning and feature-selection techniques on large datasets. Conceptualization, F.R.P.P., R.R.R., S.M., S.S., D.S.A.E., I.A.E.G., L.A., R.O. and M.A.S.A. ; data curation, K.S. and M.A.S.A. ; formal analysis, F.R.P., R.R., S.M., S.S., K.S., D.S.A.E., I.A.E.G., L.A., R.O. and M.A.S.A. ; funding acquisition, F.R.P.P. and M.A.S.A. ; investigation, F.R.P.P., R.R.R. and D.S.A.E. ; methodology, F.R.P.P., R.R.R., S.M., S.S., K.S., D.S.A.E., I.A.E.G., L.A., R.O. and M.A.S.A. ; project administration, F.R.P.P., D.S.A.E. and M.A.S.A. ; resources, F.R.P.P., S.M., S.S., K.S., D.S.A.E. and I.A.E.G. ; software, S.M. and L.A.; supervision, F.R.P., R.R., S.S., K.S., D.S.A.E. and R.O. ; validation, S.M., L.A. and M.A.S.A. ; visualization, F.R.P.P., S.M., D.S.A.E. and I.A.E.G. ; writing--original draft, F.R.P.P., R.R.R., S.M., S.S., K.S., D.S.A.E., I.A.E.G., L.A., R.O. and M.A.S.A. ; writing--review and editing, F.R.P.P., R.R.R., S.M., S.S., K.S., D.S.A.E., I.A.E.G., L.A., R.O. and M.A.S.A. All authors have read and agreed to the published version of the manuscript. This research was funded by the Deanship of Scientific Research, King Faisal University, grant number GRANT712, and the APC was funded by the Deanship of Scientific Research, King Faisal University. Not applicable.