## 5 Results
### Optimization of the Interval Type-2 Fuzzy Systems  
For the optimization of the interval type-2 fuzzy systems, 30 different experiments were carried out, using the parameters in Table 2. Experiment 21 was the one with the best classification results for the fuzzy system with trapezoidal membership functions, with 97.5% classification. In Table 7, the parameters of the aforementioned experiment are presented.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trapezoidal Membership Functions** & \multicolumn{2}{c}{**Gaussians Membership Functions**} \\ \hline BSA & CSA & BSA & CSA \\
94.58\% & 88.33\% & 90.33\% & 89.66\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of BSA and CSA averages.  
\begin{table}  
Meanwhile, for the interval type-2 fuzzy system with Gaussian membership functions, experiment 28 was the best, with a 100% classification. In Table 8, the parameters of that experiment are presented.  
Table 9 presents the percentage of classification in each experiment for both membership functions, having a classification average for interval type-2 fuzzy systems with trapezoidal membership functions of 92.92%, while for the interval type-2 fuzzy system with Gaussian membership functions an average rating of 92.78%. The best experiments are highlight in green.  
The optimized inputs with trapezoidal membership function are presented in Figures 25 and 26, while the output is presented in Figure 27.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline M & 87 \\ pop & 230 \\ DimG & 28 \\ FQ & 15 \\ c1 & 3.4 \\ c2 & 3.4 \\ a1 & 1.5 \\ a2 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Parameters of the best experiment with trapezoidal membership functions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **MFTra** & **MFGauss** \\ \hline
1 & 92.50\% & 92.50\% \\
2 & 92.50\% & 90\% \\
3 & 92.50\% & 87.50\% \\
4 & 95\% & 90\% \\
5 & 92  
Similarly, in Figures 28 and 29, the inputs of optimized interval type-2 fuzzy system with Gaussian membership functions are presented. The output of this fuzzy system is presented in Figure 30.  
Figure 27: Optimized output heart rate level with trapezoidal MFs.  
Figure 28: Optimized input age with Gaussian MFs.  
Figure 25: Optimized input age with trapezoidal MFs.  
Figure 26: Optimized input heart rate with trapezoidal MFs.  
In this case, the adjustment in the footprint of uncertainty is more noticeable than the optimizations of the type-1 fuzzy systems.  
30 experiments with the CSA were performed, using the same population and iterations presented in Table 2, and also obtaining the percentage of classification of the patients. In Table 10, the percentage of classification for each experiment is presented; column 2 presents the results with trapezoidal membership functions, while column 3 presents the results with Gaussian membership functions in both cases being presented with the interval type-2 fuzzy system.  
The average classification of the 30 experiments in the fuzzy systems with trapezoidal membership functions was 92.83%, while for the Gaussian membership functions it was 88.33%.  
In Table 11, the comparison of the averages of the experiments performed with CSA and BSA is presented, it can be seen that a higher percentage of classification was obtained when the optimization was done with the BSA.  
Figure 30: Optimized output heart rate level with Gaussian MFs.  
Figure 29: Optimized input heart rate with Gaussian MFs.