## 4 Results  
We performed feature selection based on the three feature selection methods based on genetic algorithm, information gain, and wolf search algorithm. Then, the performance of machine learning using the three feature selection methods were compared with that of machine learning not using the feature selection. In this experiment, to obtain a feature subset of 100 of 17,850 features, runs of GA1, GA2, and IG took 16,221 s, 16,056 s, and 1 s, respectively. A run of WSA took 41,273 s to obtain a subset of 73 features out of 17,850 features. Preprocessing, such as normalization or standardization, for using the selected feature as an input value of machine learning is omitted. In this study, fivefold cross-validation was used to evaluate the machine learning models. That is, 4/5, which is 80%, of the total data were used as a training set, and 1/5, which is 20%, of data were used as a test set for each fold. When classifying [PERSON]'s disease using the dataset used in this study, it was confirmed that data preprocessing reduced the accuracy of machine learning. Figure 4 shows the performance of machine learning on the four feature selection methods used in the experiment. In the case of XGBoost, the four evaluation criteria increased when performing feature selection, excluding GA2, compared to when not performing feature selection. In the case of DNN, the accuracy of non-feature selection was approximately 47%, showing lower accuracy than that of XGBoost, but the accuracy of feature selection through GA1 and WSA was approximately 85%, showing the highest performance among the machine learning. In the case of SVM and DT, it can be observed that the model's performance decreases by performing feature selection using genetic algorithm and WSA. GA1 showed performance improvements in XGBoost and DNN, GA2 showed performance improvements in DNN, IG showed performance improvements in all machine learning, and WSA showed performance improvements in XGBoost, DNN, and DT. Figures 5-8 show the performance of the four machine learning models using the ROC curves. Each figure shows the mean ROC curve of the five folds and the ROC curve for each fold. In these figures, for each machine learning model, the result with the best feature selection method and the result without feature selection were compared. Figure 4: Graph comparing the four performance indicators of feature selection methods: (**a**) XGBoost; (**b**) DNN; (**c**) SVM; and (**d**) DT. Figure 5: ROC curve of XGBoost: (**a**) non-feature selection; (**b**) IG. Figure 8: ROC curve of DT: (**a**) non-feature selection; (**b**) IG. Figure 6: ROC curve of DNN: (**a**) non-feature selection; (**b**) GA1. Figure 7: ROC curve of SVM: (**a**) non-feature selection; (**b**) IG. As shown in Figures 5 and 6, it can be observed that the performance of machine learning using feature selection improved compared to the case where feature selection was not used. For example, the average AUC of XGBoost learned through feature selection was approximately 87%, and for the DNNs, the average AUC was approximately 89%. Similarly, Figures 7 and 8 show that, for SVM and DT, the model's AUC increases when using feature selection based on IG. We also analyzed whether the results with the best feature selection for each machine learning model were statistically significant using \(t\)-tests. Table 4 shows the result. In this table, the \(p\)-value of the \(t\)-test between the result of each feature selection and that of non-feature selection was also included for statistical analysis. For each machine learning model, the best among the results of five folds is marked in bold. According to the \(t\)-test, for XGBoost, DNN, and DT, the performances of the models were improved by using feature selection methods (\(0.05>p\)). However, for SVM, there was no significant difference between the result of feature selection and that of non-feature selection, with a \(p\) value of \(0.43\) (\(0.05<p\)). model was reduced by 26.98 times.