## 3 Results
### Experiments to Diagnose Each MetS Type without a Blood Test
#### 3.3.3 Approach 3: Diagnosis of Each MetS Type Using the Dataset with Oversampling and ANN  
The approach 3 is to solve the imbalanced dataset for each MetS type. Several research articles have proposed that the balanced dataset improves prediction [61; 62], and most importantly, it improves ANN training as the model can correctly adapt to the minority feature of the data. Therefore, we used the sampling methods technique to balance the dataset to improve the representation of each MetS type [63]. For this reason, a data balancing algorithm called SMOTE [41; 42] was used with the WEKA data mining tool. In the dataset of 615 patients, the fasting plasma glucose dichotomous variable (G) was used in SMOTE to generate synthetic data [64] with a result of 799 samples (615 plus 184 synthetic data). This approach increased the prevalence of MetS to 51.81%, and improved the prevalence rate of fasting plasma glucose. It also updated the distribution of the W, P, G, H, and T risk factors. The new values were 73.72%, 29.16%, 30.79%, 65.58%, and 45.93%, respectively, as observed in Figure 9. We called the new dataset of 799 samples as dataset with oversampling. Figure 8: Performance indicators of the RusBoost for the MetS types using the original dataset. Moreover, as a result of using SMOTE in the fasting plasma glucose (G) risk factor, the percentage of the MetS types related to fasting plasma glucose increased, as shown in Figure 10. This result shows that the prevalence rate of the MetS types WPG, WGT, and WGH is greater than 10%, PGH and PGT is greater than 5%, and GHT s higher than 15% in comparison with Figure 6. The prevalence rate of WPT, WPH, WPG, WGT, WGH, WTH, PGH, PGT, PTH, and GHT was 15.39%, 18.15%, 11.89%, 17.52%, 22.9%, 29.41%, 9.01%, 8.01%, 11.14%, and 15.39%, respectively. This result generated an increment in the prevalence rate of the traditional MetS of 51.81% as well. We used an algorithm of sequential feature selection from Matlab to achieve maximum discrimination in the classification algorithms, and Table 12 shows the results. This step refined the variables' selection due to synthetic data's creation, similar to the actual data. This step increments the positive values of the MetS types related to biochemical variables, especially those related to fasting plasma glucose. As part of this refinement, the types WPG and WGT selected POD as a predictor variable. On the other hand, the BFP variable remains in both Tables 10 and 12, especially for the types WPH, WGH, and WTH. In those types, the biochemical variable HDL-C is related, and this depends on gender. The BFP variable is a function of gender, waist circumference, and age, demonstrating a logical relationship with WPH, WGH, and WTH types. Interestingly, the WPG and PGH types have the same initial predictor variables. Figure 10: Prevalence rate of the MetS types using the dataset with oversampling. Figure 9: Prevalence rate of the MetS risk factors using the dataset with oversampling. Afterward, we designed several ANN for each type of MetS according to the variables of Table 12 using the dataset with oversampling. These selected features were used as inputs of the ANN with several hidden neurons calculated using Equation (9) as shown in Table 13 and with the same configuration parameter recommended by [PERSON] [33] and was validated each ANN using random subsampling obtaining the average performance indicators as shown Figure 11.  
Figure 11 shows the ANN to classify for the diagnosis of the MetS types WPT, WPH, WPG were an outstanding ability to discriminate given that the AROC were 90.69%, 93.06%, and 90.57%, respectively with an excellent specificity rate. The MetS types PGH and PTH showed an excellent ability to discriminate AROC of 86.32% and 88.41%, respectively, with an excellent specificity rate. The PGT, WGH, and WTH types showed an acceptable level (AROC = 76.92%), (AROC = 75.52%), and (AROC = 70.38%) respectively, with a regular sensitivity rate.