## 4 Discussion  
One important issue to discuss is the AROC low level for some of the MetS types. The AROC low level in the WGT, WTH, and GHT types is not related to the prevalence of each MetS type in the balanced dataset. However, the reason could be the few predictors variables of the models. On the  
Figure 12: Performance indicators of the RusBoost for the MetS types using the dataset with oversampling. other hand, it is interesting to note that when SMOTE is used to balance a risk factor such as fast plasma glucose in a dataset for traditional MetS diagnosis, minimal improvement in AROC levels is achieved. For ANN, the difference is 73.50% compared to 73.25%. For RusBoost, the difference is 82.48% compared to 82.86% using the same variables AGE, WC, WHR, and SBP. Each medical organization established its criteria to diagnose the MetS, which varies according to the thresholds of risk factors. All the cited medical organizations have in common that for the diagnosis of the MetS, doctors must check at least three of five risk factors. Therefore, it is a combination of five risk factors, in which at least three are positive. This article is an example of the conjunction of data science with the combinatorial analysis and the simplification applications by the Quine-McCluskey algorithm for finding the segmentation of the MetS. This approach achieved the diagnosis of the MetS types without using a blood sample that is, using a non-invasive method. For example, for a patient with waist circumference, triglycerides, and increased blood pressure, the system would predict only one type of MetS (WPT) to be active and the other types to be inactive. Therefore, doctors can infer that the patient has MetS due to increased triglycerides, blood pressure, and waist circumference to help focus on initial treatment and prevent diabetes mellitus or stroke. We found that each MetS type's predictor variables are different from those of the traditional MetS. These variables were used to configure each machine learning technique to diagnose each MetS type without a blood test. We also found that the prevalence of the MetS type related to the risk factor of fasting plasma glucose has a low rate. Therefore, we performed four approaches to improve the performance indicators of the classifiers. The first approach was to use artificial neural networks to diagnose each of the MetS types working with previously selected variables, obtaining excellent AROC levels for the types of MetS such as WPG, WPT, WPH, WGH, WTH, PGT, PGH, and PTH. However, the sensitivity levels were low when diagnosing some MetS types indicating a high type 2 error level. The second approach was to use an algorithm specialized in data imbalance to compensate for the sensitivity levels and the specificity levels, thereby decreasing the ROC levels affecting four types of MetS WGT, WGH, WTH, and GHT. The third approach was to increase the sample with an oversampling algorithm such as SMOTE, allowing evaluation of the ANN models with selected variables from these new data, finding an increase in AROC levels. However, the sensitivity levels are relatively low in some types, and so, the type 2 errors decreased relatively but are still high. The fourth approach was a mixture of using an imbalanced data prediction algorithm RusBoost with the larger dataset. We found a significant improvement in the levels of AROC for the MetS types levels and, at the same time, a considerable increase in sensitivity and, therefore, decreased the type 2 error. This result favored its choice compared to the neural networks for the types WPT, WPH, WPG, WGH, PGH, PGT, PTH. This approach resulted in 7 types of MetS that can be diagnosed without using a blood test. However, the types WGH, WTH, and GHT have a regular level to diagnose it, possibly due to the few predictors variables that can be reflected in its power of discrimination.