## 2 Methodology
### Model
#### 2.3.3 Framework to Diagnose the MetS Types  
Figure 2 shows an abstract overview of the implementation of the proposed method by using a framework divided into three stages. \begin{table}
\begin{tabular}{l l} \hline \hline
**Type** & **Diagnostic of MetS** \\ \hline WPT & Increased Waist Circumference, Blood Pressure, \\  & and Triglycerides levels \\ \hline WPH & Increased Waist Circumference, Blood Pressure, \\  & and reduction of HDL-C levels \\ \hline WPG & Increased Waist Circumference, Blood Pressure, \\  & and Fasting Plasma Glucose levels \\ \hline WGT & Increased Waist Circumference, Fasting Plasma Glucose, \\  & and Triglycerides levels \\ \hline WGH & Increased Waist Circumference, Fasting Plasma Glucose, \\  & and decreased HDL-C levels \\ \hline WTH & Increased Waist Circumference, Triglycerides, \\  & and decreased HDL-C levels \\ \hline PGT & Increased Blood Pressure, Fasting Plasma Glucose, \\  & Triglycerides levels \\ \hline PGH & Increased Blood Pressure, Fasting Plasma Glucose, \\  & and decreased HDL-C levels \\ \hline PHT & Increased Blood Pressure, Triglycerides and \\  & decreased HDL-C levels \\ \hline GHT & Increased Fasting Plasma Glucose, Triglycerides and \\  & decreased HDL-C levels \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Types of MetS according to the HMS criterion. Figure 2: Framework to diagnose the types of MetS by HMS criterion using non-biochemical variables. * Extraction, Transformation, and Load (ETL) In this stage, we collected the data from a population of 615 subjects who authorized taking a blood sample to measure the values of triglycerides, HDL-C, and fasting plasma glucose. Moreover, the study recorded the anthropometric and clinical variables such as Age, Sex, Weight, Height, Waist Circumference (WC), Hip Circumference (HC), Systole Blood Pressure (SBP), and Diastole Blood Pressure (DBP). Later, through the transformation process, we obtained Body Mass Index, Body Fat Percentage, Waist Hip circumference ratio, Dichotomous Blood Pressure Systolic, Dichotomous Diastolic Blood Pressure, Dichotomous Blood Pressure, Dichotomous triglycerides, Dichotomous fasting blood sugar, Dichotomous HDL-C, and Dichotomous Waist circumference among others. Afterward, we used dichotomous values of the HMS criteria' risk factors to build the different MetS types obtained from the segmentation process explained in the previous subsection. We obtained the output variables WPG, WPH, WPT, WGH, WGT, WTH, PGT, PGH, PHT, and GHT. Therefore, all anthropometric and clinical data was loaded in a dataset of 615 records. * Statistical analysis and balancing dataset In this stage, we began with a dataset containing 615 people with samples of biochemical variables with their respective diagnostic of MetS. Then, we did a descriptive statistical analysis of the dataset, finding that some types of MetS were imbalanced, as shown in the Results section. This problem was caused by the low prevalence of the risk factor for fasting blood glucose in the study population. This low prevalence is expected in a study of MetS [40]. We resolved this imbalance by using a data balancing technique, such as the Synthetic Minority Oversampling Technique (SMOTE) [41,42] implemented by WEKA. We created synthetic data to get a balanced dataset of 799 records (615 plus 184 synthetic data) and a better distribution of risk factors of MetS, thus improving the quality of discrimination. * Modeling In this stage, we use an algorithm to select the necessary non-biochemical features. We used Sequential Feature Selection in Matlab to achieve the maximum discrimination in both datasets (imbalanced and balanced) of the proposed model's output variables. For the following step, we used several Multilayer Perceptron (MLP) ANN to predict each MetS type: WPG, WPH, WPT, WGH, WGT, WTH, PGT, PGH, PTH, and GHT. These ANN should be trained before being used to predict the output variable value, i.e., the dependent variable.