# Machine and Deep Learning Applied to Predict Metabolic Syndrome without a Blood Screening
## 3 Results
### Performance Evaluation of Classifiers
The obtained results showed that the highest value of ACC (84.12% with an SD of 0.38) was achieved by RF, using the resulting variables obtained in whole dataset, which are: waist, WHtR, DBP, BMI, SBP, weight, age, height, cola_soda, a glass of whole milk (milk), snore, c_coffe, and oax_cheese. The deep neural network had the best performance in B.ACC, with a value of 63.26% and an SD of 2.42, also using the resulting variables obtained in whole dataset. In the case of PPV, the RF obtained the best performance with a value of 85.73% and an SD of 0.32, using the resulting variables obtained by category, which are: waist, WHtR, BMI, SBP, DBP, age, cola_soda, flav_water, c_coffe, snore, nt_sleep, restless, flav_soda, and drowsy. Regarding NPV, the deep neural network achieved the best performance with a value of 85.76% and an SD of 1.11, again with the whole dataset's relevant variables. Considering the results that were obtained in the metrics by the different models, it is possible to identify that the first RF model with an mtry = 1 and ntree = 300 has the best performance using the resulting variables from the whole dataset (Waist, WHtR, DBP, BMI, SBP, Weight, Age, Height, cola_soda, milk, snore, c_coffe, and oax_cheese), even though the deep neural network with the same variables has a better performance in en B.ACC (63.26%); the difference is minimal (0.33%). Similarly, despite the fact that the RF model that uses the resulting variables by category has the highest performance in PPV (85.73%), the difference is also minimal (0.95%). Concerning men, Table 5 shows the results of performance evaluation with the results variables by category and from whole dataset applying RF, deep neural network, and C4.5, using ACC, B.ACC, PPV, and NPV as evaluation metrics, as well as their respective standard deviations of the average performance for the 30 models that are generated for each case. In this case, the results obtained by the classifiers were better than those that were obtained with women data. The RF obtained the best values in ACC (88.17% and an SD of 0.49), B.ACC (80.73% and an SD of 0.84), and PPV (92.29% and an SD of 0.36) using the variables that were obtained from the whole dataset, such as: Waist, DBP, SBP, BMI, WHtR, Weight, Height, Age, c_coffe, flav_water, tortilla, cola_soda, and snore. With respect to the best value of NPV, it was obtained by the deep neural network (91.26% and an SD of 1.79) using the variables obtained by PCC in the whole dataset, which are: age, weight, BMI, waist, WHtR, SBP, DBP, snore, cola_soda, and tortilla. In this case, the RF model presents a better performance, although its NPV was not a high as other classifiers, such as the deep neural network, and it does obtain the highest value in B.ACC, thus obtaining a better performance in balanced classifications. The relevant variables that were found by the Chi.Square filter method, both for women (Table 1B) and for men (Table 2B), were used to create predictive models with the C4.5 classifier to compare them with the models that were created with Deep Neural Network and Random Forest. Furthermore, C4.5 has the advantage of creating a model that is interpretable to the naked eye by a person. In Figure 3, we present the best model for women and in Figure 4, the best model for men, both being found over 30 independent runs using the train set. Classification trees have the property of making an embedded variable selection during the predictive model creation process. In the case of women, the variables that were selected for the construction of the tree were WAIST, SBP, DBP, AGE, BMI, and FREC080. For men, the variables selected for the construction of the tree were WAIST, SBP, DBP, WhtR, and BMI. The model shown in Figure 3 represents the best predictive model created with C4.5 for women across 30 independent runs.