International Journal of Hypertension
Volume 2019 (2019), Article ID 7320365, 13 pages
https://doi.org/10.1155/2019/7320365

Research Article

Blood Pressure Classification Using the Method of the Modular Neural Networks

Martha Pulido,¹ Patricia Melin,¹ and German Prado-Arechiga²

¹Tijuana Institute of Technology, Calzada Tecnológico, Tijuana 22379, Mexico
²Cardiodiagnostico Excel Medial Center, Tijuana 22010, Mexico

Correspondence should be addressed to Patricia Melin; pmelin@tectijuana.mx

Received 8 November 2018; Revised 27 December 2018; Accepted 13 January 2019; Published 23 January 2019

Academic Editor: Tomohiro Katsuya

Copyright © 2019 Martha Pulido et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

In this paper, we present a new model based on modular neural networks (MNN) to classify a patient’s blood pressure level (systolic and diastolic pressure and pulse). Tests are performed with the Levenberg-Marquardt (trainlm) and scaled conjugate gradient backpropagation (traincsg) training methods. The modular neural network architecture is formed by three modules. In the first module we consider the diastolic pressure data; in the second module we use details of the systolic pressure; in the third module, pulse data is used and the response integration is performed with the average method. The goal is to design the best MNN architecture for achieving an accurate classification. The results of the model show that MNN presents an excellent classification for blood pressure. The contribution of this work is related to helping the cardiologist in providing a good diagnosis and patient treatment and allows the analysis of the behavior of blood pressure in relation to the corresponding diagnosis, in order to prevent heart disease.

1. Introduction

The learning ability of neural networks and their pattern classification characteristics are the reasons why these models can be of great importance for medical applications. Nowadays there are many approaches in intelligent computing, such as evolutionary computing, fuzzy systems [1–7] and neural networks [8–18], which are used in the areas of medicine [17–22].

Hypertension that threatens to be present in most of the people of the world is a dangerous disease and leads to fatal consequences such as death and is a risk factor for people who suffer from it: obesity, diabetes mellitus, etc.

Hypertension is a global problem as it affects more than a billion people and causes more than ten million (avoidable) deaths each year. The only way to know if a person suffers from this disease is to constantly check the blood pressure and effectively diagnose and prevent this disease [23, 24].

Currently, there are several computer techniques that have been applied in medicine, such as neural networks and fuzzy systems to diagnose hypertension; by using these methods we can provide information about the factors and risks the patient may have.

The main contribution of this work is the proposed Arterial Hypertension Classification and Diagnosis model based on modular neural networks for disease prevention. In this way, the cardiologist with the help of the model may prescribe the necessary treatment to the patient since hypertension is a disease that can evolve without showing any symptoms; this is the reason it is also known as “the silent killer”.

In this work, an MNN model is used to classify the patient’s hypertension level, tests are also performed with the Levenberg-Marquardt (trainlm) and scaled conjugate gradient backpropagation (traincsg) training methods, and this neural network consists of three modules. In the first module we consider the diastolic pressure data; in the second module we use details of the systolic pressure; in the third module, pulse data is used. Therefore, we obtain the patient’s blood pressure through ambulatory blood pressure monitoring (ABPM); so far we have 300 records.

1.1. Overview of Related Works

Artificial neural networks with the back propagation learning algorithm to obtain the hypertension diagnosis were presented by Sumathi B. et al. [25]. The method was designed with eight risk factors: smoking, stress, family history, high cholesterol, etc., where the result of neural network classification shows if the patient suffers from arterial hypertension.

Huang S. et al. [26] presented a study to investigate the factors of Hypertension (HTN) and was implemented as a prediction model for 35-year-old people in a rural area of China, with a modular neural network, considering risk factors, such as socioeconomic status and education level.

Vilkov V.G. et al. [13, 27] presented a comparative study with models of daily blood pressure monitoring was performed in 34 apparently healthy subjects and 72 patients with arterial hypertension (AH). They compared the efficiency of diagnosis of latent AH using models based on artificial neural networks of different architectures.

Barman M. et al. [28] presented an intelligent system based on a fuzzy rule system, to diagnose heart diseases and the number of heart attacks; such fuzzy system has seven inputs and uses the Cleveland database.

Patil P. et al. [29] designed a sensor which measures the pulse and temperature of the patient and is controlled by a fuzzy system which shows the patient’s pulse via remote and sends a warning to relatives, doctors, or ambulances, in case it presents an emergency for patients.

Morsi I. et al. [30] presented a model to diagnose blood pressure. A group of 105 patients is used to design this model and another group with the same number of patients is used to test and thus check the efficiency of fuzzy systems in the field of medicine.

Hussein S. [31] analyzed the risk factors of hypertension and a model was designed for the prediction of rural residents over 35 years of age, considering several factors such as education level, sedentary work, and history of hypertension in the family.

Touyz M. R. et al. [32] presented an ANFIS system; methodology is designed to diagnose and compare an existing fuzzy expert system, regarding performance metrics accuracy and sensitivity.

1.2. Artificial Neural Networks

Neural networks are integrated by many interrelated components. A neural network can have a structure of multiple inputs and outputs; these systems operate similarly to the human brain. A neural network learns from input values, and this helps us learn about the human being [33–35].

1.3. Hypertension

In Mexico, a large number of professional studies have been made with the idea of determining the prevalence of hypertension, defined as the frequency of the disease at a given time in a particular place. The most important studies use different methodological criteria, which make them difficult to compare. In these studies, different blood pressure levels are used to define this disease. They even propose an adequate standardization to measure it, which usually leads to overdiagnosis. The number of measurements made at the time of the survey, be it on the same day or on different days, impacts significantly the prevalence of the disease. Another study that was decided to be included on the findings reported is the study of heart diseases of San Antonio (ECSA), which includes Mexican-American population and has a branch in Mexico City: the study of diabetes in Mexico City (EDCM). In these studies, the prevalence and incidence of hypertension and other cardiovascular factors are also reported.

1.4. Arterial Hypertension

Hypertension may be essential (unknown etiology, but with hereditary background) or secondary (with demonstrable cause) and can also be isolated or as metabolic syndrome; this disease is incapacitating and deadly due to the damage caused to important organs: blood vessels, heart, kidney, and eyes. Normal levels of blood pressure are those below 139/89 mmHg; secondary hypertension can be suspected in young people younger than 35 years of age or when there is no hypertension history in the family or in the absence of a family. Treatment of hypertension helps reducing damage to organs or even reverses it if possible; this treatment may be a drug with antidepressant use or nonpharmacological treatment, which includes changes in hygienic-dietetic habits (reduction weight, stop smoking, and drinking alcoholic beverages).

1.4.1. Development of Systolic and Diastolic Hypertension

The risk of cardiovascular complications begins, apparently with blood pressure values of 115mmHg for systolic and 75 mmHg for diastolic. In the clinical area, several subtypes of hypertension determined by isolated elevations of systolic and diastolic, or the combination of both are used. These subgroups have their own natural history and present a different cardiovascular risk.

Isolated systolic hypertension (ISH) is common after 50 years of age, affecting nearly 50% of people between 50 and 59 years of age, reaching 90% in those over 80 years old. This subtype of hypertension reflects the increase in the stiffness of the aorta and great vessels without an increase in arteriolar resistance.

When there is an increase in arteriolar resistance combined with a lack of increased arterial stiffness, the isolated diastolic pressure subtype (IDP) occurs; this subtype predominates in people younger than 40, comprising almost 60% of the population [36].

1.4.2. Pulse Pressure

Hypertension includes calculating pulse pressure (PP), which is done by subtracting the diastolic pressure and systolic quantities [37, 38]. In the elderly, increased systolic blood pressure reflects an increase in the degree of stiffness of arteries; as a result, pulse pressure increases. This is related to an increased incidence of cardiovascular events. Blood pressure PP is closely related to the changes produced by age in people over 50, increasing diastolic coronary mortality rates, and after 60 they stop. To most people, pulse and systolic pressure values become the most important risk triggers.

Previously, the pulse was measured when the examiner or physician would sit comfortably on the right side to support the patient’s elbow and, with his right thumb, explore the antecubital fosse, where the brachial artery is. The patient’s arm reflex should be activated. When the thumb or finger of the examiner is correctly in place, he or she can raise or lower the patient’s forearm by varying the pressure applied to the artery, feeling the maximum pulse. The right thumb of the examiner can feel the patient’s carotid artery similarly, which he or she may feel by gently grasping the patient’s fingertips with theirs. The digital pulse can be counted exactly by simultaneously palpating the radial artery while the examiner supports the patient’s wrist. The femoral pulse of a child of tender age should be sought only while the leg is relaxed voluntarily.

The arteries pulsations provide information about heart rhythms and speed, arterial pulse differential (right and left limbs, or top and bottom), thrills (shudders), and waveform. [39, 40].

Bradycardia. The bradycardia term simply means slower rate. Bradycardia athletes: the heart of an athlete is much more powerful than a normal person, which allows their heart force a greater volume of blood with each beat, a large proportion of blood driven into the arterial tree with each beat probably produces sufficient circulatory reflexes to begin causing bradycardia.

Tachycardia. Tachycardia means rapid heart rate. The three causes of tachycardia are increased body temperature and heart stimulation by the sympathetic and toxic states of the heart. Increased heart rate of about 10 beats per minute for each degree Celsius increases body temperature, up to 41°C; at this temperature, the heart rate may actually decrease by increasing muscle wasting as a result of fever. Tachycardia causes hyperthermia and increases the frequency of the heart rhythm [41–45].

1.5. Ambulatory Blood Pressure Monitoring

Nowadays modern laboratory methods often require outpatient-monitoring equipment to measure a variety of the indicators for blood pressure (BP), for 24 hours continuously. The biological rhythms are physiological functions and pathological alterations. The BP with an average heart rate of 72 beats per minute and 103.680 pulse waves is produced with corresponding changes in BP [46]. While at first the method was used in research studies, it is now increasingly used in clinical practice, as it provides additional data of measures from office and home. Moreover, only the ABMP can shed some light on symptomatic episodes occurring within 24 hours, either by raising or lowering the BP [47]. This means that it can be used not only for diagnosis of arterial hypertension (HA), but also to evaluate the frequency and severity of acute episodes of hyper- or hypotension. The ABMP is very useful to investigate the effects of new drugs for a period of 24 hours.

2. The Proposed Method

This section presents the proposed method for blood pressure classification, which consists of designing of modular neural networks (MNN) and the integration of responses of MNN with an average method. The main goals are to implement and find the best MNN architecture; the MNN consists of three modules; the first module is for the systolic pressure, the next module is the diastolic pressure, and in the last module we have pulse, this way we classify the arterial hypertension of a person.

Figure 1 illustrates the MNN structure, which considers the diastolic, systolic pressure, and the pulse for the MNN inputs, and in this case has 3 modules. The tests are performed by changing the number of layers that are between 1 and 3 and the number of neurons from 1 to 50, and in this way we obtained the responses of the MNN and integrate them with the average integration method and we obtained the classification of blood pressure.

Figure 1: General scheme of the method.

In Figure 2, we present the data used for the classification of the arterial hypertension, where 300 patient samples are used for training all the modules in the modular neural network and we considered other 40 patients for tests with 45 records for each patient in the complete architecture.

Figure 2: Real data of the patients.

Table 1 presents the parameters of the MNN that are manually changed to obtain the best architecture.

Table 1: MNN data.

Number of Layers
Number of Neurons
Epochs
Learning Rate
Error Goal
Training Methods

1 to 3
1 to 50
500
0.001
0.01
(i) Levenberg-Marquardt (trainlm)
(ii) Scaled Conjugate Gradient Back Propagation (traincsg).

Table 2 presents the classification of arterial hypertension according to the European guidelines.

Table 2: Blood pressure levels (mmHg).

Category
Systolic

Diastolic

Optimal
<120
And
<80

Normal
120-129
And/or
80-84

High Normal
130-139
And/or
85-89

Grade 1 Hypertension
140-159
And/or
90-99

Grade 2 Hypertension
160-179
And/or
100-109

Grade 3 Hypertension
≥180
And/or
≥110

Isolated Systolic hypertension
≥140
And/or
<90

3. Discussion and Results

The proposed method to classify the blood pressure of a patient was validated with tests performed on 16 patients and positive results were obtained for the MNN.

The results of the best MNN architecture are shown in Figure 3. For each of the modules of the MNN, the goal error was of 0.002 and 500 epochs were used; the number of neurons used was 14 in the first layer and 15 in the second layer.

Figure 3: The best architecture for MNN with the training method (trainlm).

Table 3 presents the results of the MNN with the “trainlm” method for the classification arterial hypertension.

Table 3: Classification for MNN with train method (trainlm).

No. Persons
Number of Neurons
Time
Systolic
Diastolic
Pulse
Classification

Person1
14,15
00:06:48
118
70
68
Optimal

Person 2
14,15
00:06:48
109
77
76
Optimal

Person 3
14,15
00:06:48
112
76
79
Optimal

Person 4
14,15
00:06:48
120
73
73
Optimal

Person 5
14,15
00:06:48
146
86
77
Grade 1 Hypertension

Person 6
14,15
00:06:48
107
63
92
Optimal

Person 7
14,15
00:06:48
128
83
97
Normal

Person 8
14,15
00:06:48
112
66
96
Optimal

Person 9
14,15
00:06:48
130
76
73
Normal

Person 10
14,15
00:06:48
123
79
57
Normal

Person 11
14,15
00:06:48
138
65
65
High Normal

Person 12
14,15
00:06:48
138
84
74
High Normal

Person 13
14,15
00:06:48
123
76
79
Normal

Person 14
14,15
00:06:48
114
63
78
Optimal

Person 15
14,15
00:06:48
124
79
72
Normal

Person 16
14,15
00:06:48
134
84
89
High Normal

Person 17
14,15
00:06:48
125
77
80
Normal

Person 18
14,15
00:06:48
106
65
79
Optimal

Person 19
14,15
00:06:48
110
68
79
Optimal

Person 20
14,15
00:06:48
123
76
80
Normal

Person 21
14,15
00:06:48
115
72
76
Optimal

Person 22
14,15
00:06:48
112
73
78
Optimal

Person 23
14,15
00:06:48
122
76
77
Normal

Person 24
14,15
00:06:48
117
68
90
Optimal

Person 25
14,15
00:06:48
121
74
92
Optimal

Person 26
14,15
00:06:48
130
82
89
Normal

Person 27
14,15
00:06:48
121
63
86
Optimal

Person 28
14,15
00:06:48
112
73
90
Optimal

Person 29
14,15
00:06:48
123
82
80
Normal

Person 30
14,15
00:06:48
95
61
73
Optimal

Person 31
14,15
00:06:48
108
65
72
Optimal

Person 32
14,15
00:06:48
110
70
73
Optimal

Person 33
14,15
00:06:48
116
67
71
Normal

Person 34
14,15
00:06:48
130
86
80
Normal

Person 35
14,15
00:06:48
117
73
80
Optimal

Person 36
14,15
00:06:48
117
54
81
Optimal

Person 37
14,15
00:06:48
113
74
74
Optimal

Person 38
14,15
00:06:48
132
86
79
Normal

Person 39
14,15
00:06:48
128
80
78
Normal

Person 40
14,15
00:06:48
131
85
70
Normal

Table 4 presents the average of the test of the MNN for each of the patients.

Table 4: Average results of the MNN with (trainlm).

Person
Time
Systolic
Diastolic
Pulse

Person 1
00:15:34
115
73
67

Person 2
00:15:34
105
72
77

Person 3
00:15:34
114
70
80

Person 4
00:15:34
119
71
72

Person 5
00:15:34
145
84
75

Person 6
00:15:34
104
61
90

Person 7
00:15:34
125
87
96

Person 8
00:15:34
109
64
73

Person 9
00:15:34
129
73
56

Person 10
00:15:34
122
77
65

Person 11
00:15:34
136
63
70

Person 12
00:15:34
136
81
71

Person 13
00:15:34
120
74
78

Person 14
00:15:34
110
62
77

Person 15
00:15:34
119
68
70

Person 16
00:15:34
131
82
80

Person 17
00:15:34
125
77
80

Person 18
00:15:34
106
65
79

Person 19
00:15:34
110
68
79

Person 20
00:15:34
123
76
80

Person 21
00:15:34
115
72
76

Person 22
00:15:34
112
73
78

Person 23
00:15:34
122
76
77

Person 24
00:15:34
117
68
90

Person 25
00:15:34
121
74
92

Person 26
00:15:34
130
82
89

Person 27
00:15:34
121
63
86

Person 28
00:15:34
112
73
90

Person 29
00:15:34
123
82
80

Person 30
00:15:34
95
61
73

Person 31
00:15:34
108
65
72

Person 32
00:15:34
110
70
73

Person 33
00:15:34
116
67
71

Person 34
00:15:34
130
86
80

Person 35
00:15:34
117
73
80

Person 36
00:15:34
117
54
81

Person 37
00:15:34
113
74
74

Person 38
00:15:34
132
86
79

Person 39
00:15:34
128
80
78

Person 40
00:15:34
131
85
70

The results of the best MNN architecture are shown in Figure 4. For each of the modules of the MNN, the goal error was of 0.002 and 500 epochs were use;, the number of neurons used was 26 in the first layer and 29 in the second layer.

Figure 4: The best architecture for MNN with the training method (traincsg).

Table 5 presents the results of the MNN with the “trainscg” method for the classification of arterial hypertension.

Table 5: Classification for MNN with train method (traincsg).

No. Persons
Number of Neurons
Time
Systolic
Diastolic
Pulse
Classification

Person1
26,29
00:07:12
116
72
67
Optimal

Person 2
26,29
00:07:12
106
72
77
Optimal

Person 3
26,29
00:07:12
114
70
80
Optimal

Person 4
26,29
00:07:12
119
71
72
Optimal

Person 5
26,29
00:07:12
145
84
75
Grade1 Hypertension

Person 6
26,29
00:07:12
107
61
90
Normal

Person 7
26,29
00:07:12
130
87
97
High Normal

Person 8
26,29
00:07:12
120
64
97
Optimal

Person 9
26,29
00:07:12
131
74
73
High Normal

Person 10
26,29
00:07:12
122
78
57
Normal

Person 11
26,29
00:07:12
136
63
65
High Normal

Person 12
26,29
00:07:12
135
75
70
High Normal

Person 13
26,29
00:07:12
120
75
78
Normal

Person14
26,29
00:07:12
117
72
77
Optimal

Person 15
26,29
00:07:12
121
69
70
Normal

Person 16
26,29
00:07:12
132
90
78
High Normal

Person 17
26,29
00:07:12
126
76
80
Normal

Person 18
26,29
00:07:12
106
65
82
Optimal

Person 19
26,29
00:07:12
110
68
84
Optimal

Person 20
26,29
00:07:12
123
76
90
Normal

Person 21
26,29
00:07:12
112
71
78
Optimal

Person 22
26,29
00:07:12
111
70
70
Optimal

Person 23
26,29
00:07:12
122
73
71
Normal

Person 24
26,29
00:07:12
116
67
80
Optimal

Person 25
26,29
00:07:12
120
74
80
Optimal

Person 26
26,29
00:07:12
129
80
79
Normal

Person 27
26,29
00:07:12
120
61
74
Optimal

Person 28
26,29
00:07:12
112
73
81
Optimal

Person 29
26,29
00:07:12
121
82
67
Normal

Person 30
26,29
00:07:12
95
61
77
Optimal

Person 31
26,29
00:07:12
106
65
80
Optimal

Person 32
26,29
00:07:12
116
75
72
Optimal

Person 33
26,29
00:07:12
116
71
75
Normal

Person 34
26,29
00:07:12
130
86
90
Normal

Person 35
26,29
00:07:12
117
74
97
Optimal

Person 36
26,29
00:07:12
117
58
88
Optimal

Person 37
26,29
00:07:12
113
70
73
Optimal

Person 38
26,29
00:07:12
131
71
80
Normal

Person 39
26,29
00:07:12
128
81
82
Normal

Person 40
26,29
00:07:12
134
85
81
Normal

In Table 6 the average of the test of the MNN for each of the persons is presented.

Table 6: Average of the MNN with (traincsg).

Person
Time
Systolic
Diastolic
Pulse

Person 1
00:17:19
115
73
67

Person 2
00:17:19
105
72
77

Person 3
00:17:19
114
70
80

Person 4
00:17:19
119
71
72

Person 5
00:17:19
145
84
75

Person 6
00:17:19
104
61
90

Person 7
00:17:19
125
87
96

Person 8
00:17:19
109
64
73

Person 9
00:17:19
129
73
56

Person 10
00:17:19
122
77
65

Person 11
00:17:19
136
63
70

Person 12
00:17:19
136
81
71

Person 13
00:17:19
120
74
78

Person 14
00:17:19
110
62
77

Person 15
00:17:19
119
68
70

Person 16
00:17:19
131
82
80

Person 17
00:17:19
126
76
80

Person 18
00:17:19
106
65
82

Person 19
00:17:19
110
68
84

Person 20
00:17:19
123
76
90

Person 21
00:17:19
112
71
78

Person 22
00:17:19
111
70
70

Person 23
00:17:19
122
73
71

Person 24
00:17:19
116
67
80

Person 25
00:17:19
120
74
80

Person 26
00:17:19
129
80
79

Person 27
00:17:19
120
61
74

Person 28
00:17:19
112
73
81

Person 29
00:17:19
121
82
67

Person 30
00:17:19
95
61
77

Person 31
00:17:19
106
65
80

Person 32
00:17:19
116
75
72

Person 33
00:17:19
116
71
75

Person 34
00:17:19
130
86
90

Person 35
00:17:19
117
74
97

Person 36
00:17:19
117
58
88

Person 37
00:17:19
113
70
73

Person 38
00:17:19
131
71
80

Person 39
00:17:19
128
81
82

Person 40
00:17:19
134
85
81

Figure 5 presents the modeling data of diastolic pressure for the MNN; the pink line represents the real data and the green line represents data modeled with the MNN. The results of this model for the diastolic pressure that were obtained were good with respect to the records used to use the tests, since the trend according to the cardiologist was good.

Figure 5: Modeling of diastolic pressure with the MNN.

Figure 6 presents the modeling data of systolic pressure with the MNN proposed; the pink line represents the real data and the green line represents data modeled with the MNN. The results of this model for the systolic pressure that were obtained were good with respect to the records used to use the tests, since the trend according to the cardiologist was good.

Figure 6: Modeling of systolic pressure with the MNN.

Figure 7 present the modeling data of pulse pressure with the MNN; the pink line represents the real data and the green line represents data modeled with the modular neural network. The obtained results of this model for the pulse pressure were good with respect to the records used in the tests, since the trend according to the cardiologist is good.

Figure 7: MNN modeling of pulse pressure.

4. Statistical Comparative Study

In this section a hypothesis test is made based on the errors obtained with the architecture of the modular network using the Levenberg-Marquardt learning method (trainlm) to obtain the trend of the systolic pressure. In addition, the results are compared with linear regression models based on the obtained errors.

The model used to perform the statistical comparison was the well-known linear regression. This model describes the relationship between a dependent variable and (also known as the output or answer) as a function of one or more independent variables (called predictors). The general equation corresponding to a linear regression model is as follows:

where

  represents a parameter that establishes the linear relationship between variables.  represents the random error terms.  represents the real data. y is the variable for classification.

The formulas to estimate the beta parameter values are given by

In this case the values for βo and β₁ are the following (for each of the modules):

Module 1

 βo =0.101663348532528 β₁= 1.028385618868264

Module 2

 βo = 0.04938271 β₁= 1.024901397932274

Module 3

 βo =0.565544448530641 β₁= 1.104635529315485

A set of 30 experiments are carried out to compare the results; for this, we use the parametric Z test of two samples, which is used with the following formula:

where

  is the observed difference.  is the expected difference.  is the standard error of the difference.

The null hypothesis establishes that the mean of the errors of the systolic neural network are greater than or equal to the average of the errors obtained by the regression, being the alternative hypothesis that the mean of the errors of the systolic neural network are lower than the average of the errors obtained by the regression; the parameters of the hypothesis test are shown in Table 7.

Table 7: Parameters for hypothesis testing modules.

Parameters

Confidence Interval
95%

Alfa
0.05

Ho
μ₁ ≥ μ₂

Ha
μ₁ < μ₂

Critical Value
Z= -1.645

In Table 8 we show the descriptive statistics for this test.

Table 8: Descriptive statistics for Module 1 (systolic).

Variable
Observations
Mean
Std. Derivation

MNN(sys)
30
9.820
1.997

Regression
30
16.830
4.508

Table 9 shows the results obtained by applying formula (1) for Module 1.

Table 9: Results of the Z-test for Module 1.

Difference
-7.010

z (Observed Value)
-7.788

z (Critical Value)
-1.645

p-value
<3.33066907387547x10⁻¹⁵

Alfa
0.05

Since the result of the p value is lower than the level of significance alpha = 0.05, we reject the null hypothesis and accept the alternative hypothesis, so we can conclude that there is sufficient evidence with a 5% level of significance to support the claim that the means of the errors of the modular neural network for the obtaining of the systolic pressure tendency are smaller than those obtained by the regression method.

In Table 10 the descriptive statistics for Module 2 (diastolic) test is shown.

Table 10: Descriptive statistics Module 2 (diastolic).

Variable
Observations
Mean
Std. Derivation

MNN(sis)
30
23.177
3.096

Regression
30
34.778
5.438

Table 11 shows the results obtained by applying formula (1) for this test.

Table 11: Results of the Z-test for Module 2.

Difference
-9.462

z (Observed Value)
-8.2383

z (Critical Value)
-1.645

p-value
<1.11022302462516x10⁻¹⁶

Alfa
0.05

Since the result of the p value is lower than the level of significance alpha = 0.05, we reject the null hypothesis and accept the alternative hypothesis, so we can conclude that there is sufficient evidence with a 5% level of significance to support the claim that the means of the errors of the modular neural network for the obtaining of the diastolic pressure tendency are smaller than those obtained by the regression method.

We show in Table 12 the descriptive statistics for Module 3 (pulse) test.

Table 12: Descriptive statistics Module 3 (pulse).

Variable
Observations
Mean
Std. Derivation

MNN(sis)
30
14.774
1.821

Regression
30
28.367
4.733

Table 13 shows the results obtained by applying formula (1) for the third module (pulse tendency).

Table 13: Results of the Z-test Module 3.

Difference
-13.593

z (Observed Value)
-14.682

z (Critical Value)
-1.645

p-value
<2.59524148975464x10⁻¹⁷

Alfa
0.05

Since the result of the p value is less than the level of significance alpha = 0.05, we reject the null hypothesis and accept the alternative hypothesis, so we can conclude that there is sufficient evidence with a 5% level of significance to support the claim that the means of the errors of the modular neural network for obtaining the pulse tendency are smaller than those obtained by the regression method.

When comparing the model of the modular neural network with the linear regression models by means of the z-statistic tests, we can realize that when using intelligent computing techniques, in this case the modular neural networks, we have a more efficient technique to classify the systolic and diastolic pressure and the pulse and this could help the cardiologist detect and prevent diseases in the blood.

5. Conclusion

In this paper we have obtained good results with the proposed model. The MNN classification model for arterial hypertension was implemented with two training methods for the modular neural network, namely, the Scale Conjugate Gradient Backpropagation (trainscg) and Levenberg-Marquardt (trainlm), and we achieved good results with the second method (trainlm). Good results were also obtained in the diastolic, systolic, and pulse models, since the trend was good with respect to the records used to perform the tests. In addition, we have made a comparison between the neural network model and the regression equations, showing that the MNN model statistically outperforms the regression model. In this paper we conclude that this classification method is effective and could help the cardiologist to detect and prevent a patient’s blood pressure.

Data Availability

The data that was used in this research to support the findings of this study are available from the corresponding author upon request by email pmelin@tectijuana.mx.

Conflicts of Interest

The authors declare that there are no conflicts of interest, financial or nonfinancial, with respect to this research study.

Authors’ Contributions

The three authors of the paper were responsible for (1) concept and design of the system, (2) acquisition of data, (3) analysis and interpretation of data, and (4) preparation of the manuscript. The authors have agreed to authorship the paper and the order of authorship for this manuscript of this research.

Acknowledgments

We would like to express our gratitude to the CONACYT for Research Grant no. 246774 and Tijuana Institute of Technology for the facilities and resources granted for the development of this research.

References

A. A. Abdullah, Z. Zakaria, and N. F. Mohammad, “Design and development of fuzzy expert system for diagnosis of hypertension,” in Proceedings of the 2nd International Conference on Intelligent Systems, Modelling and Simulation (ISMS '11), vol. 56, pp. 113–117, IEEE, Kuala Lumpur, Malaysia, January 2011.

A. A. Abdullah, Z. Zakaria, and N. F. Mohammad, “Design and development of fuzzy expert system for diagnosis of hypertension,” in Proceedings of the 2nd International Conference on Intelligent Systems, Modelling and Simulation (ISMS '11), vol. 10, pp. 131–141, IEEE, Kuala Lumpur, Malaysia, January 2011.

R. Kaur and A. Kaur, “Hypertension Diagnosis Using Fuzzy Expert System,” International Journal of Engineering Research and Applications (IJERA), pp. 2248–9622, 2014.

R. Poli, S. Cagnoni, G. Coppini, and G. Valli, “A Neural Network Expert System for Diagnosing and Treating Hypertension,” The Computer Journal, vol. 24, no. 3, pp. 64–71, 1991.

R. Fuller and S. Giove, “A Neuro-Fuzzy Approach to FMOLP Problems,” in Proceedings of CIFT94, pp. 97–101, Trento, Italy, 1994.

R. Nohria and P. S. Mann, “Diagnosis of Hypertension using Adaptive Neuro-Fuzzy Inference System,” IJCST, vol. 6, pp. 2229–4333, 2015.

A. Zeinab and T. Hamid, Design of a Fuzzy Expert System and A Multi-layer Neural Network System for Diagnosis of Hypertension, vol. 2, Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran, 2015.

R. Yafawi, M. E. Knauft, K. Stokem, J. M. Palminteri, and M. WirthJ, “Pulmonary arterial hypertension,” Encyclopedia of Cardiovascular Research and Medecine, pp. 181–194, 2018.

M. S. Kallistratos, L. E. Poulimenos, and A. J. Manolis, “Atrial fibrillation and arterial hypertension,” Pharmacological Research, vol. 128, pp. 322–326, 2018.

C. Cuspidi, M. Tadic, G. Grassi, and G. Mancia, “Treatment of hypertension: The ESH/ESC guidelines recommendations,” Pharmacological Research, vol. 128, pp. 315–321, 2018.

A. Corrado, M. Correale, N. Mansueto et al., “Nailfold capillaroscopic changes in patients with idiopathic pulmonary arterial hypertension and systemic sclerosis-related pulmonary arterial hypertension,” Microvascular Research, vol. 114, pp. 46–51, 2017.

P. Srivastava, A. Srivastava, A. Burande, and A. Khandelwal, “A note on hypertension classification scheme and soft computing decision making system,” ISRN Biomathematics, vol. 11, pp. 13–22, 2013.

S. Huang, Y. Xu, L. Yue et al., “Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area,” Hypertension Research, vol. 33, no. 7, pp. 722–726, 2010.

P. Srivastava, A. Srivastava, A. Burande, and A. Khandelwal, “A note on hypertension classification scheme and soft computing decision making system,” ISRN Biomathematics, vol. 12, Article ID 342970, pp. 20–23, 2013.

M. Ture, I. Kurt, A. Turhan Kurum, and K. Ozdamar, “Comparing classification techniques for predicting essential hypertension,” Expert Systems with Applications, vol. 29, no. 3, pp. 583–588, 2005.

P. Melin, I. Miramontes, and G. Prado-Arechiga, “A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis,” Expert Systems with Applications, vol. 107, pp. 146–164, 2018.

P. Melin and G. Prado-Arechiga, New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension, Springer, 2018.

J. C. Guzman, P. Melin, and G. Prado-Arechiga, “Design of an optimized fuzzy classifier for the diagnosis of blood pressure with a new computational method for expert rule optimization,” Algorithms, vol. 10, no. 3, p. 79, 2017.

S. Das, P. K. Ghosh, and S. Kar, “Hypertension diagnosis: A comparative study using fuzzy expert system and neuro fuzzy system,” in Proceedings of the 2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013, vol. 148, pp. 113–121, Durgapur, India, July 2013.

X. Y. Djam and Y. H. Kimbi, “Fuzzy expert system for the management of hypertension,” Pacific Journal of Science and Technology, vol. 11, pp. 390–402, 2011.

R. E. Klabunde, Cardiovascular Physiologic Concepts, Wolters Kluwer Health, 2nd, 2011.

I. K. Ludmila and F. Steimann, Fuzzy Medical Diagnosis, School of Mathematics, University of Wales, Banggor, UK, 2008.

H. Zhang and F. C. Lin, “Medical Diagnosis by the Virtual Physician,” IEEE Xplore Computer Based Medical System, pp. 296–302, 1999.

Y. Clec'h, C. Vicaut, E. Marbeuf-G et al., “Can fuzzy logic make things more clear?” Critical Care, vol. 3, no. 1, pp. 116–120, 2009.

M. R. Grübler, M. Gaksch, K. Kienreich et al., “Effects of Vitamin D3 on asymmetric- and symmetric dimethylarginine in arterial hypertension,” The Journal of Steroid Biochemistry and Molecular Biology, vol. 175, pp. 157–163, 2018.

B. Sumathi and A. Santhakumaran, “Pre-diagnosis of hypertension using artificial neural network,” Global Journal of Computer Science and Technology, Coimbator, Tamil Nadu, vol. 3, no. 6, pp. 21–32, 2011.

V. G. Vilkov, R. G. Oganov, and S. A. Shal'nova, “Comparative accuracy of neural network models for diagnosing latent arterial hypertension on the basis of data on daily blood pressure monitoring,” Human Physiology, vol. 32, no. 6, pp. 657–661, 2006.

A. Kaur and A. Bhardwaj, “Genetic neuro fuzzy system for hypertension diagnosis,” International Journal of Computer Science and Information Technologies, vol. 5, no. 4, pp. 4986–4989, 2014.

M. Barman and J. Choudhury, “A fuzzy rule base system for the diagnosis of heart disease,” International Journal of Computer Applications, pp. 46–53, 2012.

P. Patil and S. Mohsin, “Fuzzy Logic based Health Care System using Wireless Body Area Network,” International Journal of Computer Applications, vol. 80, no. 12, pp. 46–51, 2013.

I. Morsi and Y. Z. Abd El Gawad, “Fuzzy logic in heart rate and blood pressure measuring system,” in Proceedings of the 8th IEEE Sensors Applications Symposium, SAS 2013, pp. 113–117, IEEE, Galveston, Tex, USA, February 2013.

S. Hosseini, C. Jutten, and S. Charbonnier, “Neural network modeling of ambulatory systolic blood pressure for hypertension diagnosis,” in Artificial Neural Nets Problem Solving Methods, vol. 2687 of Lecture Notes in Computer Science, pp. 599–606, Springer, Heidelberg, Germany, 2003.

R. M. Touyz, N. N. Lang, J. Herrmann, A. H. Van Den Meiracker, and A. H. J. Danser, “Recent Advances in Hypertension and Cardiovascular Toxicities With Vascular Endothel ial Growth Factor Inhibition,” Recent Advances in Hypertension, pp. 1–3, 2017.

L. Feng, A. H. Khan, I. Jehan, J. Allen, and T. H. Jafar, “Albuminuria and kidney function as prognostic marker of left ventricular mass among South Asians with hypertension,” Journal of the American Society of Hypertension, vol. 11, no. 12, pp. 811–822.e2, 2017.

J. L. Nierenberg, C. Li, J. He et al., “Blood Pressure Genetic Risk Score Predicts Blood Pressure Responses to Dietary Sodium and Potassium: The GenSalt Study (Genetic Epidemiology Network of Salt Sensitivity),” Hypertension (Dallas, Tex. : 1979), vol. 70, no. 6, pp. 1106–1112, 2017.

J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice Hall, 1996.

C. Torlasco, A. Faini, E. Makil et al., “Cardiovascular risk and hypertension control in Italy. Data from the 2015 World Hypertension Day,” International Journal of Cardiology, vol. 243, pp. 529–532, 2017.

G. Beevers, G. Y. H. Lip, and E. O'brien, “Blood pressure measurement: Part I—Sphygmomanometry: Factors common to all techniques,” BMJ, vol. 322, no. 7292, p. 981, 2001.

D. Bernstein, “Evaluation of the cardiovascular system: history and physical,” in Evaluation, R. M. Kliegman, B. F. Stanton, and J. W. St. Geme, Eds., Nelson Textbook of Pediatrics, pp. 385–450, 2015.

Harrison, “Tachyarrhythmias,” in Principles of Internal Medicine, vol. 2, pp. 2226–2232, McGraw-Hill, 19a edition, 2006, Section 14.

“American Heart Association,” 2016, https://www.heart.org/.

D. L. Simel, L. Goldman, and A. I. Schafer, “Approach to the Patient: History and Physical Examination,” in Goldman's Cecil Medicine, 2012.

“World Health Organization,” International Society of Hypertension Group, 2003.

J. A. Whitworth, “World Health Organization (WHO)/International Society of Hypertension (ISH) statement on management of hypertension,” Journal of Hypertension, vol. 21, no. 11, pp. 1983–1992, 2003.

G. Parati and G. Mancia, “Ambulatory Blood Pressure Monitoring,” in Manual of Hypertension, G. Mancia, J. Chalmers, S. Julius et al., Eds., pp. 153–171, Churchill-Livingstone, London, UK, 2002.

M. Sokolow, “Ambulatory blood pressure a personal historical account,” American Journal of Hypertension, vol. 6, no. 6, pp. 1615–1655, 1993, J Hypertension; 20: 1917-1923.

G. Parati and G. Mancia, “Ambulatory blood pressure monitoring in clinical practice,” Journal of Hypertension, vol. 20, no. 10, pp. 1925–1927, 2002.