## 5 Conclusions  
Healthcare professionals diagnose the Metabolic Syndrome through 5 factors, two of which they get in a medical consultation: Waist Circumference level (W) and blood pressure level (P). However, Triglyceride, HDL-C, and fasting plasma glucose levels (T, H, G) require a blood test. When we analyzed the segmentation of the MetS in types, we observed that for the diagnosis, an algorithm requires three risk factors, and we proved which risk factors generate that disease. Therefore, we suggest that in the future, MetS studies should take them into account to know which MetS type a patient has.  
The present work uses information that doctors can collect from medical history and the medical visit. Such data includes Previous Obesity Diagnosis (POD), Age, Height, Weight (WG), Waist Circumference (WC), Hip Circumference (HC), Systolic and Diastolic Blood Pressure (SBP, DBP), Body Fat Percentage (BFP), and Body Mass Index. We used an algorithm of sequential feature selection and compared machine learning techniques such as ANN and RusBoost to diagnose the several types of MetS without doing a blood test. This discovery helps in an early screening of one or several MetS types through anthropometric and clinical data using non-invasive methods. From this point, doctors can take relevant actions to change them through habits modification.  
We performed four approaches to obtain the best results. The first approach was carried out using clinical data from 615 subjects of selected variables to evaluate the ANN, obtaining excellent levels of AROC in the WPG, WPH, WPT, WGH, WTH, PGT, PGH, and PTH MetS types. However, the sensitivity levels were regular, presenting a considerable rate of type 2 errors due to the data imbalance. The second approach used a classifier for imbalanced data such as RusBoost, which improved the sensitivity levels to diagnose each MetS type, decreasing the type 2 error rate. However, the regular AROC levels decreased particularly for the classifiers for the WGT, WGH, WTH, and GHT types.  
The third approach was to use the SMOTE technique to balance the data, and in this way, we achieved an improved performance of the ANN classifiers. However, in some classifiers, the sensitivity levels were regularly presenting a considerable rate of type 2 errors. The fourth approach was to use the balanced data and the RusBoost technique. This approach generated for the MetS types WPT, WPH, WPG, WGH, PGT, and PTH the following excellent AROC levels: 89.20%, 91.49%, 85.36%, 71.08%, 84.20%, 78.39%, and 84.10%, respectively, and with high sensitivity rates. The fourth approach obtained the best results for most MetS types, but for classifying the traditional MetS, the third approach was the best.  
In the future, we plan to test the framework to diagnose the MetS types using an ANN model with ten output classification neurons (one for each type of MetS) as well as fuzzy logic, Bayesian networks, and other machine learning techniques.  
Conceptualization: M.B., E.N., M.J., and P.V.; formal analysis: M.B.; Methodology: M.B., and M.J.; software: M.B.; data curation: M.B., E.N., and P.V.; visualization: M.B., and M.J.; supervision: E.N., and P.V. All authors have read and agreed to the published version of the manuscript.  
The first author received a Doctoral scholarship from the Administrative Department of Science, Technology, and Innovation--COLCIENCIAS of Colombia.  
The first author expresses his deep thanks to the Administrative Department of Science, Technology, and Innovation--COLCIENCIAS of Colombia for the Doctoral scholarship. In addition, the author express their deep thanks to the Universidad del Norte and Universidad Autonoma del Caribe. The authors express their thanks to the reviewers of the article for their valuable comments and insights regarding the quality of the article.  
The authors declare no conflict of interest.