## 2 Methodology
### Review  
The Review phase performed a specialized search in the following databases: DBLP, IEEE, and ACM for the relationship with Computer Science, and Pubmed for its relationship with healthcare research. We have used a window of 12 years since 2008. The keywords used are \"SEGMENTATION\" OR \"TYPES\" OR \"PREDICTION\" OR \"ANN\" AND [\"METABOLIC SYNDROME\" AND \"WITHOUT BLOOD TEST\"] for Query1: IEEE, Query2: DBLP, Query3: ACM, and Query4: PUBMED to know all the projects related to keywords in these search engines dedicated to the field of engineering. The results of the queries (QUERY1: 92, QUERY2: 51, QUERY3: 82, and QUERY4: 131) provided an extensive list of journal articles and conferences. However, not all the documents are directly related to the segmentation of MetS since DBLP, IEEE XPLORE, ACM, and PUBMED delivered articles on machine learning and computer science at a general level, for example, the topic image segmentation. Then, the delivered list was filtered, evaluating its relationship with the segmentation of MetS or types of MetS. It excluded repeated articles (mirror articles). Afterward, we performed a manual inspection and then filtered by the criteria established on the following questions. * Could authors predict the Metabolic Syndrome types or segmentation without a blood test? Y/N. * What Metabolic Syndrome diagnostic criteria did the authors use? e.g., ATP II, IDF, HMS, or other criteria recognized. * What ANN configuration did the authors use? * What validation method did the authors use? e.g., hold out, random subsampling, and others. * What performance indicators did the authors use? e.g., Sensitivity, Area Under the ROC curve, Specificity. The manual inspection did not find anything about the segmentation of MetS using only variables obtained in a medical consultation such as anthropometric and clinical or history variables. However, we found three articles that diagnose MetS using ANN and anthropometric and clinical variables such as Age, Sex, Weight, Height, Waist Circumference (WC), Hip Circumference (HC), Waist to Hip ratio (WHHR), Waist to Stature (WSR), Body Mass Index (BMI), Body Fat Percentage (BFP), Systole Blood Pressure (SBP) and Diastole Blood Pressure (DBP). These variables will be used to build the model to diagnose the MetS types without doing a blood test, i.e., without using biochemical variables. As a summary, Table 2 shows an overview of the variables that are used explicitly in the study and other variables that were not used (implicitly) but are necessary to construct the explicit variables. It also shows the classification models used for the MetS diagnosis without taking a blood sample of each article in the literature, and we describe them now:  
Figure 1: RAMAD methodology [30]. [PERSON] et al. [31] configured an Artificial Neural Network (ANN) based on Multilayer Perceptron (MLP) with back-propagation of 25 hidden neurons to diagnose with HMS criteria and using BMI, WC, Weight, Height, and Sex variables from a dataset with 826 people to validate using 70% for training and 30% for testing. [PERSON] et al. [32] used anthropometric and clinical variables such as HC, Age, BMI, WC, WHR, Sex, SBP, and DBP, and implicitly used Weight and Height as inputs of a back-propagation neural network (BPNN). They diagnosed the MetS and compared the results with another machine learning technique, Principal Component Logistic Regression (PCLR), for predicting Met with IDF criteria using a dataset of 2074 individuals (male: 1495, female: 579), obtaining improved results in the BPNN. [PERSON] et al. [33] presented a feed-forward ANN with back-propagation for diagnosing the MetS with IDF criteria using non-invasive variables such as sex, age, BMI, WHR, SBP, DBP, and in an implicit way WC, Weight, Height, due to the use of WSR and BMI. The dataset of 2928 people was divided into three parts with the proportion 80:10:10 for the stage of training, validation, and testing. Diagnosing MetS using non-biochemical variables is an approach that implies not taking blood samples.