## 7 Conclusions  
In this study, we have developed a new model using Neuro-Fuzzy Hybrid techniques that actually implements the human reasoning using a set of decision rules for the study of different diseases such as Hypertension Blood pressure (HBP). This new Neuro-Fuzzy Hybrid Model (NFHM) provides us a faster, safer and accurate tool for an objective diagnostic without inter-observer variability, based in this case on the classification of Hypertension, according to the definitions of the European Guidelines. This method is very efficient therefore takes less time and is more accurate for classify the level of HBP. What can help health systems and health workers, especially in developing countries, who do not have enough specialists for Hypertension like Cardiologists, internists etc. to improve the degree of accuracy in the diagnosis of this disease with the consequent better opportunity at the start of pharmacological management and dietary hygiene measures. We are aware that this is only the beginning of the implementation of NFHM for the diagnosis of various cardiovascular diseases. In this paper, we use Ambulatory Blood Pressure Monitoring (HBPM) for the study of the BP and are aware that there are more parameters that need considered to be a more accurate diagnosis of Hypertension (HTN) such as different types of patterns like Dipper, Variability, load pressure, etc., And finally, we hope that this work, those who have preceded us, and future work will serve as motivation for other researchers to work on artificial intelligent techniques for applying it in medicine.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Patient** & **Systolic** & **Diastolic** &
\begin{tabular}{c} **Optimized** \\ **Classifier 4** \\ \end{tabular} & **Fuzzy Percentage** & **ESH BP\_Leves Table** \\ \hline
1 & 139 & 84 & High normal & 61.3 & High normal \\
2 & 135 & 90 & Grade 1 & 62.5 & Grade 1 \\
3 & 160 & 98 & Grade 2 & 74 & Grade 2 \\
4 & 177 & 110 & Grade 3 & 84.3 & Grade 3 \\
5 & 142 & 85 & **Ish grade 1** & 61.3 & Ish grade 1 \\
6 & 160 & 89 & Ish grade 2 & 71.8 & Ish\_grade 2 \\
7 & 182 & 89 & Ish grade 3 & 83.2 & Ish\_grade 3 \\
8 & 85 & 50 & Hypotension & 10.2 & Hypotension \\
9 & 110 & 70 & Optimal & 36.6 & Optimal \\
10 & 125 & 82 & Normal & 55.2 & Normal \\
11 & 135 & 85 & High normal & 60.8 & High normal \\
12 & 159 & 94 & Grade 1 & 71.8 & Grade 1 \\
13 & 175 & 105 & Grade 2 & 79.3 & Grade 2 \\
14 & 180 & 110 & Grade 3 & 84 & Grade 3 \\
15 & 110 & 80 & Normal & 52 & Normal \\
16 & 128 & 89 & High normal & 56.9 & High normal \\
17 & 158 & 77 & Ish grade1 & 66.4 & Ish grade 1 \\
18 & 150 & 108 & Grade 2 & 82.9 & Grade 2 \\
19 & 199 & 95 & Grade 3 & 87.8 & Grade 3 \\
20 & 179 & 99 & Grade 2 & 81.6 & Grade 2 \\
21 & 181 & 100 & Grade 3 & 82.6 & Grade 3 \\
22 & 210 & 90 & Grade 3 & 87.4 & Grade 3 \\
23 & 140 & 100 & Grade 2 & 75.8 & Grade 2 \\
24 & 159 & 120 & Grade 3 & 87.7 & Grade 3 \\
25 & 178 & 115 & Grade 3 & 87.8 & Grade 3 \\
26 & 140 & 80 & Ish grade 1 & 59.7 & Ish grade 1 \\
27 & 150 & 89 & Ish grade 1 & 65.2 & Ish grade 1 \\
28 & 179 & 80 & Ish grade 2 & 73.8 & Ish grade 2 \\
29 & 179 & 89 & Ish grade 2 & 81.6 & Ish grade 2 \\
30 & 199 & 82 & Ish grade 3 & 77.8 & Ish grade 3 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Shows the results of the 30 patients who were monitored and classified in the classifier optimized with GA.  
**Acknowledgments:** We would like to express our gratitude to the CONACYT and Tijuana Institute of Technology for the facilities and resources granted for the development of this research.  
**Author Contributions:** [PERSON] proposed the neuro-fuzzy computational model, the methodology and analysis of results. [PERSON] implemented the model and obtained the simulation results. [PERSON] provided the expert knowledge for developing the fuzzy rules and verified the accuracy of the diagnosis results.  
**Conflicts of Interest:** The authors declare no conflicts of interest.