## 2 Materials and Methods  
### Subjects and Dataset  
We obtained the data for this study from the Korea National Health and Nutrition Examination Survey (KnthANES V and VI) from 2010 to 2015. The KNthANES is a cross-sectional survey conducted initially by the Korea Centers for Disease Control and Prevention [39; 40; 41]. Datasets from the survey were approved by the Korea Ministry of Health and Welfare (2010-02 CON-21-C, 2011-02 CON-06-C, 2012-01 EXP-01-2C, and 2013-07 CON-03-4C). National Health and Nutrition Examination was conducted without deliberation by the Research Ethics Review Committee, as it corresponds to research conducted by the state for public welfare, according to Article 2 (1) of the Bioethics Law and Article 2 (2) 1 of the Enforcement Regulations of the same law. The research was conducted in accordance with principles of the Helsinki Declaration update of 2008. This study was approved by the Institutional Review Board of the Korea Research Institute of Standards and Science,and included approval for the access and analysis of open-source data from the KNHANES V and VI with a waiver for the documentation of informed consent (IRB No. KRISS-IRB-2019-14). MS was defined by the presence of three or more of the five conditions mentioned below [42]. The conditions are as follows (Table 1):  
We conducted an experimental study in three stages. First, we performed data integration based on the measured data (spirometric factors) from 2010 to 2015. Second, we extracted data according to the participants' demographic and clinical characteristics, including age, sex, alcohol consumption, smoking, income, anthropometric factors, blood parameters, and spirometric factors. MS was defined relative to five conditions, and we performed data translation and clarification (addressing missing, uncalculated, and unconverted values). Finally, we performed data standardization for comparison and analysis. In the statistical analysis, we implemented three methods to analyze the associations of various indices with MS. A t-test was conducted to evaluate significant differences between men and women. We conducted a binary logistic regression analysis to identify significant associations with the measured variables (anthropometric and spirometric factors and blood parameters) and determined differences between normal subjects and those with MS. We used an area under the curve (AUC) from the receiver operating characteristic (ROC) curve to assess whether significant improvement in the identification of MS was achieved based on anthropometric factors, blood parameters, and spirometric factors. Since no method exists to calculate the AUC in a complex sampling analysis, we analyzed the recognized performance by the general AUC analysis. Finally, we constructed a prediction model using machine learning algorithms based on the feature selection method for predictive power by sensitivity, 1-specificity, precision, and AUC [43]. Figure 1 shows the design of this study for preprocessing and statistical analysis. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Subject** & **Conditions** & **Disease** \\ \hline Waist circumference & \begin{tabular}{c} Men: \(\geq\)102 cm (\(\geq\)40 inches) \\ Women: \(\geq\)88 cm (\(\geq\)35 inches) \\ SBP: \(\geq\)130 mmHg \\ DBP: \(\geq\)85 mmHg \\ \(\geq\)150 mg/dL (1.7 mmol/L) \\ HDL-cholesterol \\ \end{tabular} & \
\begin{tabular}{c} Abdominal obesity \\ DBP: \(\geq\)85 mmHg \\ \(\geq\)150 mg/dL (1.7 mmol/L) \\ Men: \(<\)40 mg/dL (1.03 mmol/L) \\ Women: \(<\)50 mg/dL (1.3 mmol/L) \\ \(\geq\)100 mg/dL \\ \end{tabular} &
\begin{tabular}{c} Abdominal obesity \\ DBP: Hypertension \\ Hypert: hyperglycemia \\ Dyslipidemia \\ Diabetes \\ \end{tabular} \\ \hline SBP, systolic blood pressure; HDL-cholesterol, high-density lipid cholesterol; DBP, diastolic blood pressure. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Conditions of metabolic syndrome. Figure 1: The metabolic syndrome prediction study was designed for preprocessing and statistical analysis.