## 1 Introduction
For the population of Colombia, according to the HMS criterion, which is the most updated, the waist circumference levels in men must be \(\geq\)90 cm and in females must be \(\geq\)80 cm to have central obesity. The other risk factors are shown in Table 1. \begin{table}
\begin{tabular}{l l} \hline \hline
**Risk Factors** & **HMS Criteria** \\ \hline Central Obesity & Waist Circumference (WC) \\  & population and country specific \\ \hline Triglycerides (TG) & \(\geq\)150 mg/dL \\ \hline Fasting Plasma & \\ Glucose (FPG) & \(\geq\)100 mg/dL \\ \hline High-Density Lipoprotein & \(<\)40 mg/dL in males \\ Cholesterol (HDL-C) & \(<\)50 mg/dL in females \\ \hline Blood Pressure & Systolic \(\geq\) 130 mmHg \\  & and/or Diastolic \(\geq\) 85 mmHg \\ \hline Diagnostic criteria & Three risk factors \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definition of the MetS according to HMS (Data from [16]). Independently of the criteria to diagnose MetS, the diagnosis uses five factors. Doctors get two of those factors (Waist Circumference (WC) and Blood Pressure (BP)) in medical consultations and a community setting. Invasive tests are required to know the value of triglycerides, HDL-C, and fasting plasma glucose present in the patient's blood. Therefore, the time of treatment initiation can vary according to the health system. The delay between initial consultation, a blood test to measure the triglycerides, fasting blood sugar, and HDL-C levels, plus a diagnostic consultation, can add several days or weeks [24; 25] creating a problem to diagnose early. This time delay in obtaining the results, particularly for patients in remote locations, might be sufficient time in some cases to worsen or aggravate the patient's health conditions due to the occurrence of a stroke diabetes [26]. It is useful to diagnose early MetS to avoid or delay the onset of some illnesses already mentioned. Many researchers have proposed solving the problem without doing a blood test using machine learning techniques, such as [PERSON] et al. [27], [PERSON] et al. [28] and others. However, when diagnosing MetS, doctors always check the triglycerides, fasting plasma glucose, and HDL-C values to recommend a specific treatment to prevent diabetes or coronary diseases. They also want to know the possible cause since it allows a better decision to plan patient treatment [23; 29]. Therefore, this article proposes segmenting MetS into various types to identify the risk factors that produce it and use machine learning to diagnose them early without making a blood test and comparing each MetS type with the traditional MetS. We used four approaches for improving the accuracy or AROC for the different MetS types. The first approach uses the ANN technique; the second approach uses an ensemble classification algorithm as the Random undersampling Boosted tree (RusBoost) ensemble. The third approach uses an oversampling technique to create more data and then applying ANN. The last approach uses the dataset with oversampling and RusBoost. The objectives of this paper are the following:  
* Achieve a mathematical representation to diagnose MetS using HMS criteria. * Propose a segmentation of MetS using HMS criteria. * Develop a framework to diagnose the different MetS types according to HMS criteria using a set of variables that doctors can obtain using non-invasive methods in a first consultation. * Evaluate two machine learning techniques using performance indicators for each MetS type. We now continue to explain the methodology to design a framework to diagnose each MetS type without doing a blood test based on MetS segmentation. Then, we show the results of the implementation and then the discussion and conclusion.