## 3 Results
Among all factors, TG (AUC = 0.787 [0.775-0.799]) showed strong predictive power in men, whereas WHtR (AUC = 0.813 [0.802-0.823]) demonstrated the highest AUC value in women. GUL and HBA1C exhibited strong predictive powers among the blood parameters in men (AUC = 0.776 [0.764-0.789] and AUC = 0.712 [0.697-0.726], respectively) and women (AUC = 0.810 [0.799-0.821] and AUC = 0.776 [0.765-0.788], respectively). Among the spirometric factors, FEV6 and FVC showed strongest negative predictive power for men (AUC = 0.413 [0.398-0.429] and AUC = 0.423 [0.407-0.438], respectively) and women (AUC = 0.460 [0.447-0.474] and AUC = 0.465 [0.452-0.479], respectively). Figure 4: AUC analysis of metabolic syndrome in women. Figure 3: AUC analysis of metabolic syndrome in men. Comparison of the Predictive Power of MS with Anthropometric Factors, Blood Parameters, Urinary Parameters, and Spirometric Factors  
Each prediction model was constructed using NB and logistic regression algorithms by a subset of features using the wrapper and filter feature selection method for performance evaluation of the MS prediction model for both men and women. Table 5 shows detailed results of feature selection by the wrapper and filter method. The model built by Wrapper NB in men included 13 features: age, WT, WC, SBP, GLU, HDL, TG, ALT, WBC, USG, UCREA, FVC, and FEV6. Our results revealed a higher predictive power in women than in men for NB and logistic regression predictions. The performance of models was compared and evaluated based on sensitivity, 1-specificity, F-measure, AUC, area under the precision-recall curve (AUPRC), and root mean square error (RMSE) [47]. As shown in Table 6, for men, the sensitivity, 1-specificity, and F-measure of the prediction model (Wrapper LR [Logistic Regression]) were 0.926, 0.449, and 0.882 in the normal group and 0.551, 0.074, and 0.633 in the MS group, respectively. Among women, the sensitivity, 1-specificity, and F-measure were 0.930, 0.261, and 0.911 in the normal group and 0.739, 0.070, and 0.778 in the MS group, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sex** & **Prediction Model** & **Class** & **Sensitivity** & **1-Specificity** & **Precision** & **F-Measure** \\ \hline \multirow{8}{*}{Men} & Wrapper NB & Normal & 0.904 & 0.488 & 0.826 & 0.863 \\ \cline{2-7}  & Metabolic & 0.512 & 0.096 & 0.674 & 0.582 \\ \cline{2-7}  & Wrapper LR & Normal & 0.926 & 0.449 & 0.841 & 0.882 \\ \cline{2-7}  & & Metabolic & 0.551 & 0.074 & 0.742 & 0.633 \\ \cline{2-7}  & Filter NB & Normal & 0.919 & 0.598 & 0.798 & 0.854 \\ \cline{2-7}  & & Metabolic & 0.402 & 0.081 & 0.659 & 0.499 \\ \cline{2-7}  & Filter LR & Normal & 0.924 & 0.469 & 0.835 & 0.877 \\ \cline{2-7}  & & Metabolic & 0.531 & 0.076 & 0.730 & 0.615 \\ \hline \multirow{8}{*}{Women} & Wrapper NB & Normal & 0.905 & 0.262 & 0.888 & 0.896 \\ \cline{2-7}  & & Metabolic & 0.738 & 0.095 & 0.770 & 0.754 \\ \cline{1-1} \cline{2-7}  & Wrapper LR & Normal & 0.930 & 0.261 & 0.892 & 0.911 \\ \cline{1-1} \cline{2-7}  & & Metabolic & 0.739 & 0.070 & 0.821 & 0.778 \\ \cline{1-1} \cline{2-7}  & Filter NB & Normal & 0.923 & 0.334 & 0.864 & 0.893 \\ \cline{1-1} \cline{2-7}  & & Metabolic & 0.666 & 0.077 & 0.788 & 0.722 \\ \cline{1-1} \cline{2-7}  & Filter LR & Normal & 0.927 & 0.275 & 0.886 & 0.906 \\ \cline{1-1} \cline{2-7}  & Metabolic & 0.725 & 0.073 & 0.811 & 0.766 \\ \hline \hline \end{tabular}  
* This table was created using data transformed by standardization. The results of detailed classification performance were grouped by class (normal and metabolic groups) using a confusion matrix. NB: [PERSON], LR: logistic regression, metabolic: metabolic syndrome.  
\end{table} 
Table 5: Feature selection using the wrapper and filter method for each model. As shown in Table 7, among the men MS prediction models, Wrapper_LR's AUC [95% CI], AUPRC [95% CI], R2 [95% CI], and RMSE [95% CI] were the predictive power with 0.868 [0.860-0.876], 0.712 [0.701-0.723], 0.960 [0.955-0.965], and 0.358 [0.346-0.370], respectively.