## 3 Results
### Experiment to Diagnose the Traditional MetS without Biochemical Variables  
We found in the review several articles such as [PERSON] [31], [PERSON] [33], and and [PERSON] [32] using ANN to diagnose MetS without biochemical variables. So, we conducted several experiments to compare the models described by the authors in those articles [31; 32; 33]. We used the data from the study of 615 subjects from the Universidad del Norte. Table 2 shows the variables used to build the ANN of each article. We analyzed the training and test distributions of each of the following articles in chronological order, as published by [PERSON] [31], [PERSON] [32], and [PERSON] [33]. For example, [PERSON] [31] and [PERSON] [32] used 70/30, and [PERSON] [33] used 80/10/10, as explained in the review section. We homogenized and compared all the experiments using a feed-forward Artificial Neural Network (ANN) with back-propagation of 3 layers perceptron and with the training data (70% of the data) and the testing data (30% of the remaining data). For the validation, we used the random subsampling technique of 100 times. We implemented an ANN with 25 hidden neurons as published by [31]. It is important to note that [PERSON] [31] did not publish all the configuration parameters of the ANN, only the number \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Variables** & **MetS m(SD)** & **No MetS m(SD)** & **Total m(SD)** & \(p\) \\ \hline Age (year) & 47.62 (17.49) & 38.89 (15.96) & 42.61 (17.17) & \textless{}0.001 \\ WC (cm) & 99.81 (11.33) & 87.24 (11.91) & 92.59 (13.21) & \textless{}0.001 \\ HC (cm) & 105.51 (10.56) & 93.73 (12.50) & 98.75 (13.07) & \textless{}0.001 \\ Weight (Kg) & 79.08 (17.11) & 66.59 (13.81) & 71.71 (16.43) & \textless{}0.001 \\ Height (m) & 1.64 (0.09) & 1.62 (0.09) & 1.63 (0.09) & 0.068 \\ BMI (Kg/m) & 29.09 (5.31) & 25.26 (4.74) & 26.89 (5.33) & \textless{}0.001 \\ WHR \({}^{*}\) & 0.94 (0.05) & 0.93 (0.09) & 0.94 (0.08) & \textless{}0.001 \\ BFP (\%) & 38.64 (8.46) & 30.86 (10.23) & 34.05 (10.28) & \textless{}0.001 \\ SBP (mmHg) & 128.52 (18,46) & 112.91 (12,61) & 119.55 (17.19) & \textless{}0.001 \\ DBP (mmHg) & 78.48 (11.13) & 71.18 (9.21) & 74.29 (10.69) & \textless{}0.001 \\ \hline \hline \end{tabular}  
* (cm/cm):Average(m); Standard deviation (SD).  
\end{table} 
Table 8: Statistical description of the study variables for the total data. of hidden neurons, and so, we used the configuration set by [PERSON] [33] using the parameters shown in Table 9 to build the ANN because they were the only one who published the configuration. [PERSON] [31] used the variables WC, Sex, Height, Weight, and BMI. The results for this ANN show sensitivity 69.44%, specificity 63.78%, and AROC 74.8% with random subsampling validation of 100 times and a ratio of 70% for training data and 30% for testing data due to the low prevalence of MetS. On the other hand, [PERSON] [32] used the variables SEX, AGE, BMI, WC, HC, WHR, SBP, and DBP and an ANN of 5 hidden neurons. We implemented and tested it using the same configuration to homogenize and compare, resulting in sensitivity 75.37%, specificity 72.54%, and AROC 81.75% using HMS criteria. We built the ANN published by [PERSON] [33] but we changed the distribution of training and testing data (70% for distribution and 30% for testing). We used the random subsampling validation of 100 times, obtaining a mean of the sensitivity of 71.62%, a specificity of 66.95%, and AROC of 74.94%, for ANN with 96 hidden neurons. Moreover, using the ANN of 85 hidden neurons, we obtained a mean sensitivity 72.22%, specificity 66.25%, and AROC 74.79%. In this article, we used an algorithm of sequential feature selection by Matlab [58; 59] with 17 variables from the set of variables detailed in Table 2 obtaining a set of AGE, WC, WHR, and SBP variables to achieve the maximum discrimination in the classification algorithms. The number of hidden neurons was calculated with Equation (9), resulting in 4 with the same configuration parameters by [PERSON] [33].