## 6 Conclusions  
In this study, we compared various feature selection methods for machine learning algorithms to classify [PERSON]'s disease better. We experimentally validated that various feature selection methods improved the performance of machine learning when classifying [PERSON]'s disease using RNA-seq data. For XGBoost and DNN, feature selection methods based on GA1, IG, and WSA improved the performance when classifying [PERSON]'s disease. The accuracy of classification by XGBoost increased by 10.00%, from 77.27% to 87.27%, with the IG-based feature selection method. Similarly, the accuracy of DNN also increased with feature selection methods based on WSA and GA1 by 38.18%, from 47.27% \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Number of Features** & **Model** & **Accuracy (\%)** & **Precision** & **Recall** & \(\mathbf{F_{1}}\) & **Time Cost (s)** \\ \hline \multirow{4}{*}{Non-feature selection} & \multirow{4}{*}{17,850} & XGBoost & 77.27 & 76.60 & 76.44 & 77.03 & 26.45 \\ \cline{3-7}  & & DNN & 47.27 & 50.00 & 32.06 & 23.64 & 16.74 \\ \cline{3-7}  & & SVM & 80.91 & 80.20 & 80.43 & 82.52 & 38.28 \\ \cline{3-7}  & & DT & 70.00 & 68.62 & 68.59 & 70.51 & 11.44 \\ \hline \multirow{4}{*}{GA1} & \multirow{4}{*}{75} & XGBoost & 80.91 & 80.94 & 80.53 & 81.36 & 1.02 \\ \cline{3-7}  & & 75 & DNN & **85.45** & 86.28 & **85.00** & **85.24** & 10.84 \\ \cline{2-7}  & & 50 & SVM & 79.09 & 79.69 & 78.92 & 78.64 & 0.07 \\ \cline{2-7}  & & 50 & DT & 69.09 & 68.68 & 68.12 & 67.77 & 0.09 \\ \hline \multirow{4}{*}{GA2} & \multirow{4}{*}{75} & 50 & XGBoost & 74.55 & 76.18 & 74.56 & 73.73 & 1.12 \\ \cline{2-7}  & & 75 & DNN & 84.55 & 86.69 & 83.67 & 83.72 & 4.86 \\ \cline{2-7}  & & 50 & SVM & 73.64 & 74.67 & 73.68 & 72.63 & 0.35 \\ \cline{2-7}  & & 50 & DT & 68.18 & 69.33 & 68.70 & 67.66 & 0.08 \\ \hline \multirow{4}{*}{IG} & \multirow{4}{*}{50} & XGBoost & **87.27** & 87.79 & **86.84** & **86.79** & 0.98 \\ \cline{2-7}  & & 100 & DNN & 81.82 & 83.96 & 80.83 & 81.16 & 6.56 \\ \cline{2-7}  & & 50 & SVM & **81.82** & **82.67** & **81.49** & **81.32** & 0.06 \\ \cline{2-7}  & & 50 & DT & **77.27** & **78.90** & **77.37** & 76.43 & 0.08 \\ \hline \multirow{4}{*}{WSA} & \multirow{4}{*}{73} & XGBoost & 86.36 & **89.04** & 84.95 & 85.46 & 0.77 \\ \cline{2-7}  & & 73 & DNN & **85.45** & **87.18** & 84.50 & 84.89 & 4.61 \\ \cline{1-1} \cline{2-7}  & & 8358 & SVM & 80.00 & 81.55 & 79.10 & 79.33 & 5.85 \\ \cline{1-1} \cline{2-7}  & & 73 & DT & **77.27** & 77.32 & 77.15 & **76.88** & 0.09 \\ \hline \hline \end{tabular}  
* The best result among the experimented feature selection methods for each machine learning method is marked in bold.  
\end{table} 
Table 5: Comparison of accuracy, precision, recall, \(F_{1}\) score, and time cost of machine learning for feature selection methods and non-feature selection. to 85.45%. In SVM and DT, feature selection methods based on IG and WSA showed good performance compared to GA-based feature selection. With IG-based feature selection, the classification accuracy of SVM increased from 80.91% to 81.82%, while that of DT increased from 70.00% to 77.27%. The results of these experiments confirmed that feature selection is effective for classification of [PERSON]'s disease using RNA-seq data. In addition, the computational costs were reduced for all machine learning models tested when using feature selection. The experimental results demonstrate that appropriate feature selection could improve the performance of machine learning and reduce computational costs. For future research, it will be necessary to develop an optimization algorithm that can shorten the time cost for feature selection and further improve the performance of machine learning. Conceptualization, J.K. and Y.Y. ; methodology, J.K. and Y.Y. ; software, J.K.; validation, J.K., H.-J.P. and Y.Y. ; formal analysis, J.K. and Y.Y. ; investigation, H.-J.P. and Y.Y. ; resources, J.K. and Y.Y. ; data curation, Y.Y. ; writing--original draft preparation, J.K.; writing--review and editing, J.K. and Y.Y.