Sensor based electromagnetic waves for unblocks clogged arteries

Abstract

A significant advance has been made in medical diagnosis through introduction of mathematical theories and artificial intelligence (AI). The main theme of this research work is to presents a novel arteries unblocks sensor model based on the principle of the propagation electromagnetic waves. Sensor model based on an electromagnetic waves transmit shock waves through guide wires to specifically cross calcified and fibrotic tissues in the arterial vascular system while leaving intact the elastic wall of healthy vessels. This sensor model has been implemented in a designed system model in an electric circuit inside stockings. The observed results are presented to illustrate the performances of his ability to move strange objects which represents the suspended cholesterol. The results expressed by the distance traveled by the stone under the influence of electromagnetic waves transmitted in water showed that the application of this principle for a period of time allows the opening of blockage of the arteries which demonstrates the performance of the proposed sensor model and the entire system. For health state monitoring: severity and localization coordinates of the arteries unblocks by incorporating artificial intelligence tools are studied in detail in second part for better obstruction treatment.

Keywords

Atherosclerosis arteries electromagnetic waves sensor unblocks

1. Introduction

Blocked arteries, also known as Atherosclerosis, is the build-up of fibrous and fatty material inside the arteries and is the underlying condition that causes coronary heart disease and other circulatory diseases. Atherosclerosis can affect all of the arteries, but particularly those that supply blood to the heart (coronaries), the neck arteries that supply blood to the brain (carotids), and the arteries that supply the legs (peripheral). This can ultimately bring on symptoms such as chest pain (angina) or lead to life-threatening conditions such as a heart attack or stroke.

Clogged arteries greatly increase the likelihood of heart attack, stroke, and even death. Because of these dangers, it is important to be aware, no matter how old you are, of the causes of artery plaque and treatment strategies to prevent serious consequences [1–6].

The regular diagnostic approach of Coronary Artery Disease (CAD) relies on coronary angiogram test [4], echo-cardiogram ram (ECG), nuclear scan test and exercise stress test. ECG and exercise stress do not produce sustainable results for CAD prediction due to their non-invasiveness properties and numerous biases.

CAD causes annually a millions of deaths and billions of dollars in financial losses worldwide.

Coronary artery disease (CAD) in an advanced stage and widespread becomes very difficult or almost impossible to treat using traditional revascularization methods to reconstruct the walls or tissues of blood vessels. This has allowed other techniques to enter in the world of medicine in general and to be used in the field of arterial blockages in particular. Electromagnetic fields and applied artificial intelligence (AI) techniques began to invade the diagnosis of arterial sclerosis paper published [7–9] for early detection and accurate diagnosis.

The intrusion of artificial intelligence techniques (support vector machine (SVM), artificial neural network (ANN) … etc.), in clinical decision support systems (CDSS) and their use within devices that allowed acceleration of diagnosis and accuracy of results [10]. This paper conducted a comprehensive and multifaceted review of all relevant studies that were published during 27 years between 1992 and 2019 for ML-based CAD diagnosis. This paper handled all the impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail, also the important challenges and shortcomings of ML-based CAD diagnosis are well discussed.

For physical therapy of CAD, we find electromagnetic fields (EMF); the power of the magnet is one of the most basic powers in nature. Magnetism is the force that keeps order in the galaxy, allowing stars and planets to spin at significant velocities. And in a sense, our own planet’s magnetic field is responsible for protecting all life on earth. Many veterinarians have been aware of biomagnetic benefits for years, and use magnets to heal fractures quickly, thereby saving the lives of race horses and other animals [11].

Electromagnetic fields (EMF) of lower frequencies up to 200 MHz is commonly used in medicine for diagnosis and therapy. EMF is classified according to frequency and type of field. Static magnetic fields do not vary in time, while time-varying EMF up to 100 kHz is classified as low frequency (LF) fields. Above 100 kHz and up to 300 GHz, it is referred to as RF fields. Patients are exposed to EMF from specific medical devices when undergoing diagnosis and/or therapy [12].

EMF of different frequencies may be useful for the treatment of cancer, since cancer cells use high amount of iron and are more susceptible to magnetic fields and for the treatment of malaria a low-intensity extremely-low frequency magnetic field can be used to induce vibration of hemozoin, a super-paramagnetic polymer particle, inside malaria parasites [13].

Among the references that dealt with electromagnetic fields in diagnosis of arterial sclerosis [14] presents a review of cardiac shock-wave therapy (CSWT) performed using a shock-wave (SW) generator system coupled with a cardiac ultrasound imaging system in the treatment of coronary artery disease, the analysis demonstrated a clinically significant improvement of exercise capacity and clinical variables including angina class.

Extracorporeal pulsed electromagnetic feld (PEMF) has been shown the ability to improve regeneration in various ischemic episodes. Here, we examined whether PEMF therapy facilitate cardiac recovery in rat myocardial infarction (MI), and the cellular/molecular mechanisms underlying PEMF-related therapy was further investigated indicate that extracorporeal PEMF treatment increases cardiac systolic function through inhibiting cardiac apoptosis and stimulating neovascularization new clinical strategies for ischemic vascular diseases [15].

Reference [16] summarizes the available methods for diagnosis atherosclerotic coronary artery disease in the symptomatic patient and provides an overview of the current evidence behind functional and anatomical approaches.

Atherosclerotic disease within coronary arteries causes disruption of normal, laminar flow and generates flow turbulence. The characteristic acoustic waves generated by coronary turbulence serve as a novel diagnostic target and new technology sensor. Coronary turbulence is a novel diagnostic target being studied as a potential method to detect CAD. Acoustic detection (AD) systems are based on the premise that the faint auditory signature of obstructive CAD can be isolated and analyzed to provide a new approach to noninvasive testing [17].

An interesting study focuses on electromagnetic properties of the arterial blood flow. Magnetic feld, transmitting by the oscillate blood particles, besides the flow, creates additional energy, enabling the spontaneous chemical reactions proceed across the cell membranes. Blood motion in the heart chambers and arteries has the additional basis, besides the heart contraction: rotating blood particles in the heart chambers and in the arterial branching sites, with the concomitant oscillating electric feld triggered from the heart, forms additional electromagnetic repulsing force for the charged particles, providing to the flow [18].

Based on valuable research in the field of electromagnetism next paragraph explains how electromagnetic waves can travel in the blood and, with time, move cholesterol stone.

2. Principle of asensor based electromagnetic wave

Due to the wave propagation on the surface of the water, a floating object has been attracted from one specific point to another. A very important note that can be used.

The theory of Stokes drift [19], which represents “the motion of matter transport associated with the propagation of a wave in a material medium”; One wonders if more powerful waves could deflect a giant ship from its cape … Can you imagine a world in which you would have the ability to move objects at will? On the basis of this principle, an experiment was carried out; see Fig. 1, to solve the problem of arterial blockage.

Fig. 1.

Principle of sensors’ based on electromagnetic waves to unblock clogged arteries.

A technique that transmits shock waves through guide wires to specifically cross calcified and fibrotic tissues in the arterial vascular system while leaving intact the elastic wall of healthy vessels. The challenge, with traditional angioplasty methods, is that during the procedure, one sometimes finds oneself at the walls of vessels very hardened by the accumulation of limestone, in particular.

It is sometimes impossible to cross the block because it is too hard, which prevents us from proceeding with the rest of the treatment. The intervention is then a failure.

The shock electromagnetic waves guide could help us overcome such blockages successfully.

For the surgeons, it was a real problem to bump into hardened walls. Crossing an occlusion with a guide wire is like going through a brick wall with a cooked spaghetti noodle. It is very difficult and it is long [20]. Our strategy is that we generate shock waves with a device outside the patient that are transmitted inside the guide, which is a hammer effect at the end of the guide.

3. Mathematical modeling of propagation

We consider a cylindrical pipe of constant section S, of axis x^′x, contains fluid which, at rest, is at the pressure P₀, at temperature T₀; its density is 𝜌₀ (see Fig. 2). We consider a slice of fluid which, at rest, is situated between the abscissas x and x + dx.

Fig. 2.

Cylindrical pipe of constant section.

The passage of the acoustic wave is accompanied by an overall displacement of the molecules contained in the plane of abscissa x: let 𝜉 (x, t) be this displacement at time t; thus the slice of fluid considered is at the instant t between the planes x + 𝜉 (x, t) and x + dx+𝜉 (x + dx, t). We will note in a comparable way:

–

v (x, t) the speed of movement of the section of abscissa x at time t;

–

p (x, t), the overpressure linked to the passage of the wave in x to t; thus the pressure is written $\begin{eqnarray}P(x,t)=P_{0}+p(x,t);\end{eqnarray}$

𝜌(x, t), the density of the fluid at abscissa x at time t.

We will limit ourselves to movements of small amplitudes; thus the displacement 𝜉(x, t), the overpressure p (x, t), the variation of the density (𝜌(x, t) − 𝜌₀) and their derivatives can be considered as infinitely small of the first order. In the following we will neglect all the infinitely small ones of higher order or equal to two.

A wave is a disturbance (oscillation, deformation) which propagates in the medium or a disturbance that is maintained in the medium (standing wave). An acoustic (or sound) wave is a type of wave that propagates in material medium Elastic (air, but also water, walls, earth…). The disturbance of the environment is formed by a small displacement or a small deformation of matter.

We suppose that the signal (the perturbation) has a vector character (displacement, electric field). Let u be the direction of propagation.

Propagation of an acoustic wave in a pipe of constant section containing a single fluid,

At time t the new thickness dx^′ of the considered slice is $\begin{eqnarray}\displaystyle dx^{\prime }=[x+dx+{\xi}(x+dx,t)]-[x+{\xi}(x,t)]=\left(1+\frac{\partial {\xi}}{\partial x}\right)dx. & & \displaystyle\end{eqnarray}$ (1)

The relative variation in the volume of the section is $\begin{eqnarray}\displaystyle V & = & \displaystyle S\cdot dx,\end{eqnarray}$ (2) $\begin{eqnarray}\displaystyle \partial V & = & \displaystyle S(dx^{\prime }-dx)\end{eqnarray}$ (3) $\begin{eqnarray}\displaystyle \frac{\partial V}{V} & = & \displaystyle \frac{\partial {\xi}}{\partial x}.\end{eqnarray}$ (4)

The evolution of the slice of fluid considered is assumed to be adiabatic. The volume of the slice varies by δV and its pressure by P. These quantities being of the first order and the adiabatic transformation, we have $\begin{eqnarray}\displaystyle \left(\frac{\partial V}{\partial P}\right)_{S}=\frac{\partial V}{P}. & & \displaystyle\end{eqnarray}$ (5)

The adiabatic compressibility coefficient of a fluid is defined by $\begin{eqnarray}\displaystyle {\chi}_{s} & = & \displaystyle -\frac{\partial V}{\partial P}\end{eqnarray}$ (6) $\begin{eqnarray}\displaystyle p & = & \displaystyle -\frac{1}{{\chi}_{s}}\frac{\partial V}{V}\end{eqnarray}$ (7) $\begin{eqnarray}\displaystyle p & = & \displaystyle -\frac{1}{{\chi}_{s}}\frac{\partial {\xi}}{\partial x}.\end{eqnarray}$ (8)

Note the negative sign in (8) which corresponds to the fact that the volume decreases when the pressure increases (𝜒_S > 0).

The pressure forces, measured along Ox, which act on the slice under consideration, at time t, are:

–

On the left side $\begin{eqnarray}\displaystyle F_{g}=[P_{0}+p(x+{\xi}(x,t))]\cdot S. & & \displaystyle\end{eqnarray}$ (9)

–

On the right side $\begin{eqnarray}\displaystyle F_{d}=-[P_{0}+p(x+dx+{\xi}(x+dx,t))]\cdot S. & & \displaystyle\end{eqnarray}$ (10)

The forces acting on the slice under consideration, measured along Ox, at the instant t, is given by $\begin{eqnarray}\displaystyle dF & = & \displaystyle F_{g}+F_{d}\nonumber\\ \displaystyle & = & \displaystyle -[p(x+dx+{\xi}(x+dx,t))-p(x+{\xi}(x,t))]\cdot S\nonumber\\ \displaystyle & = & \displaystyle -\frac{\partial p}{\partial x}[dx+{\xi}(x+dx,t)-{\xi}(x,t)]\cdot S\nonumber\\ \displaystyle & = & \displaystyle -\frac{\partial p}{\partial x}\left(1+\frac{\partial {\xi}}{\partial x}\right)\cdot S\cdot dx\nonumber\\ \displaystyle & = & \displaystyle -\frac{\partial p}{\partial x}\cdot S\cdot dx.\end{eqnarray}$ (11)

Note the negative sign in (11) which corresponds to the fact that the force is negative when the pressure increases with x.

We apply the fundamental principle of dynamics (Newton’s second law) to the slice of fluid considered. The mass of the slice is dm = 𝜌₀Sdx. Its acceleration following Ox is at instant t $\begin{eqnarray}\displaystyle {\gamma}=\frac{\partial ^{2}{\xi}}{\partial t^{2}}(x+{\xi}(x,t),t)=\frac{\partial ^{2}{\xi}}{\partial t^{2}}(x,t). & & \displaystyle\end{eqnarray}$ (12)

By neglecting higher order terms. The equation 𝛾dm = dF $\begin{eqnarray}\displaystyle {\rho}_{0}\frac{\partial ^{2}{\xi}}{\partial t^{2}}\cdot S\cdot dx=-\frac{\partial p}{\partial x}\cdot S\cdot dx. & & \displaystyle\end{eqnarray}$ (13)

We substitute p given by Eq. (3) in Eq. (5) $\begin{eqnarray}\displaystyle {\rho}_{0}\frac{\partial ^{2}{\xi}}{\partial t^{2}}=-\frac{\partial p}{\partial x}=\frac{1}{{\chi}_{s}}\cdot \frac{\partial ^{2}{\xi}}{\partial x^{2}}. & & \displaystyle\end{eqnarray}$ (14)

The quantity 𝜉(x, t) satisfies d’Alembert’s equation $\begin{eqnarray}\displaystyle \text{} & \text{} & \displaystyle \frac{1}{c^{2}}\cdot \frac{\partial ^{2}{\xi}}{\partial t^{2}}-\frac{\partial ^{2}{\xi}}{\partial x^{2}}=0\end{eqnarray}$ (15) $\begin{eqnarray}\displaystyle \text{} & \text{} & \displaystyle c=\frac{1}{\sqrt{{\rho}_{0}{\chi}_{s}}}.\end{eqnarray}$ (16)

The general solution of this equation is (Alembert’s theorem) $\begin{eqnarray}\displaystyle {\xi}(x,t)=F\left(t-\frac{x}{c}\right)+G\left(t+\frac{x}{c}\right). & & \displaystyle\end{eqnarray}$ (17)

Let us derive Eq. (15) with respect to x and divisions by 𝜒_S: $\begin{eqnarray}\displaystyle \text{} & \text{} & \displaystyle \displaystyle \left[\frac{1}{c^{2}}\cdot \frac{\partial ^{2}}{\partial t^{2}}-\frac{\partial ^{2}}{\partial x^{2}}\right]\left[-\frac{1}{{\chi}_{s}}\frac{\partial {\xi}}{\partial x}\right]=0\end{eqnarray}$ (18) $\begin{eqnarray}\displaystyle \text{} & \text{} & \displaystyle \displaystyle \frac{1}{c^{2}}\cdot \frac{\partial ^{2}p}{\partial t^{2}}-\frac{\partial ^{2}p}{\partial x^{2}}=0.\end{eqnarray}$ (19)

Let us derive Eq. (15) with respect to t $\begin{eqnarray}\displaystyle \text{} & \text{} & \displaystyle \left[\frac{1}{c^{2}}\cdot \frac{\partial ^{2}}{\partial t^{2}}-\frac{\partial ^{2}}{\partial x^{2}}\right]\cdot \frac{\partial {\xi}}{\partial t}=0\end{eqnarray}$ (20) $\begin{eqnarray}\displaystyle \text{} & \text{} & \displaystyle \left[\frac{1}{c^{2}}\cdot \frac{\partial ^{2}v}{\partial t^{2}}-\frac{\partial ^{2}v}{\partial x^{2}}\right]=0.\end{eqnarray}$ (21)

Since, $\begin{eqnarray}\displaystyle v=\frac{\partial {\xi}}{\partial t}. & & \displaystyle\end{eqnarray}$ (22)

The quantities p (x, t) and v (x, t) therefore satisfy the same propagation equation as 𝜉(x, t).

The experimental data are collected in Electrical Engineering Laboratory (LGEPC) of national polytechnical school of Constantine (ENPC). An experimental study was performed on small basin, magnetic circuit of 2X300 turns and 4 A, a multimeter, and variable feed circuit 50 Hz connected to the electrical magnetic circuit and a commercial field measuring sensor connected to a computer work with F-TRAS2 software (Fig. 5).

We gradually increase the voltage applied to the magnetic circuit and read in parallel the value of the current we should not exceed the value of 4 amps so as not to damage the coil of magnetic circuit (Fig. 6).

The field measuring device is placed at the bottom of the small basin and on the top of the two coils to read the magnetic field and represent it in the form of hysteresis loss by software F-TRAS2 (Figs 6 and 7). Note: This field measuring device gives values of magnetic induction B in Gauss and not Tesla, where 1 Tesla = 10 000 Gauss.

The purpose of the first part of this study is to measure the displacement and the time of a floating particle on the surface of the net water, and saline water, depending on the applied magnetic field.

The results showed in Figs 8 and 9 and Table 1 prove that electromagnetic waves can move a floating object.

The observed results illustrate the ability of electromagnetic wave to move strange objects (the stone of cholesterol). The results showed that the application of this principle for a period of time allows the opening of blockage of the arteries which demonstrates the performance of the proposed model and the entire system.

4. The procedure of health state monitoring

Research studies focused on the detection of arterial obstruction based on several variables and identifying risk factors of CAD from free-text medical records. References [21] and [22] predicts the extent of coronary artery disease based thickness score from carotid and femoral arteries, Ref. [23] predicts risk of coronary artery disease from medical dataset records. Detection of arterial obstruction is from arterial blood pressure measurement presented in [24].

Medical diagnosis involves identifying illness or disorder in a patient through physical examination, medical tests or other procedures while therapy is the treatment of physical, mental or behavioral problems and it is meant to cure or rehabilitate the sick. However, the system lacks the capability for global access due to its offline nature and could not handle vague (imprecise) data which are inherent in medical records.

The majority of the works carried out in this direction mostly deal with the structured data and identifying risk. How about predicting it?

To track health and avoid critical situations, we will adopt artificial intelligence techniques based neural network (NN) using one of the following Atherosclerosis symptoms as variables; Artificial intelligence techniques used is divided into two phases. The first one, named training phase, consists in determining the feature space (a pattern vector), the decision space (the clusters) and developing a decision rule that produces boundaries between classes. The second one, named decision phase, consists in associating an unknown pattern with one of the defined clusters, according to the decision rule. The accuracy of pattern recognition (PR) is based on the relevance of the pattern vector, i.e. choice of features contained in this vector. The feed-forward artificial neural network (FFNN) adopted in the procedure of health state monitoring for the two cases (heart disease present and heart disease absent), has 12 inputs, a hidden (with 10 neurons) and an output layer. The structure of the FFNN for discriminating and predict heart disease is 12-10-1 (inputs layer node number-hidden layer node number-output layer node number); the hyperbolic tangent sigmoid transfer function as the transfer function for the hidden layer, the linear transfer function as the transfer function for the output layer, the Levenberg-Marquardt back-propagation as the network training function, the gradient descent learning function as the weight learning function, and the mean squared error function as the performance evaluation function. The inputs to the ANN are the real feature values and the output of the ANN is the binary decision made.

Some people may have signs and symptoms of the Atherosclerosis disease. Signs and symptoms will depend on which arteries are affected as follows; the variable used in this study is the measurement of oxygen in the blood; to know and evaluate the rate of narrowing in the arteries (Fig. 3); The data set used came from the University of California Irvine data repository and is used to predict heart disease. Patients were classified as having or not having heart disease based on cardiac catheterization, the gold standard. If they had more than 50% narrowing of a coronary artery they were labeled as having heart disease. In this cohort, there are 270 patients and there are 12 independent predictive variables or column attributes. The attributes are explained on the website: https://archive.ics.uci.edu/ml/datasets/Heart+Disease.

The training date set constituted of two classes the 1st class named PRESENCE means heart disease present and encoded with the number “1”. The second class named ABSENCE present the absent of heart disease and encoded with the number “0”. The one class is constituted of 7 samples, the samples are males, aged between 40 and 70 years;

Fig. 3.

Narrowing in the arteries.

Fig. 4.

General diagram procedure classification by NN.

Fig. 5.

The experimental test bench.

Where each sample consists of 12 variables (symptoms) that represents the inputs of FFNN to represent the narrowing of the arteries.

sex: sex (1 = male; 0 = female)

chest pain type: Value 1: typical angina, Value 2: atypical angina, Value 3: non-anginal pain and Value 4: asymptomatic

BP: resting blood pressure (in mm Hg on admission to the hospital)

cholestoral: serum cholestoral in mg/dl

FBS over 120: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)

EKG results: resting electrocardiographic results

Max HR: maximum heart rate achieved

Exerice angina: exercise induced angina (1 = yes; 0 = no),

ST depression: having ST-T wave abnormality; Exercise induced ST segment depression is considered a reliable ECG finding for the diagnosis of obstructive coronary atherosclerosis.

Slope of ST: the slope of the peak exercise ST segment: Value 1: upsloping; Value 2: flat and Value 3: downsloping

Number of vessels fluro: number of major vessels (0–3) colored by flourosopy

Thallium: thal: 3 = normal; 6 = fixed defect; 7 = reversable defect.

The labeling structure of training-dataset and test-data set are presented in Tables 2 and 4 respectively; after building the neural network (NN), determine the shape of the structure, the type of training program, error calculation function and the threshold, the number of layers at the input, middle and end, we train the NN structure created on the database; The results (outputs) of training and test data set based neural network application on available data are presented in Tables 3 and 5 respectively.

The neural networks work as follows, first the training stage on a set of data, then testing stage on the testing data set does not belong in the set of training data, but always one of the symptoms leads to CAD.

We train the neural network on this input data, and the output is either heart disease or not, encoded with 1 or 0, this is the binary output corresponding to the outputs heart disease “presence” or no heart disease “absence”; Table 1 presents the results of training stage presented by two quantities “output simulation” and “Neural Network-error”, which completely agree with the target. The Target is what should the result be?

Fig. 6.

Magnetic circuit.

Fig. 7.

Magnetic field sensor and current sensor.

Fig. 8.

The characteristic B = f (H) for net water (left) and net water with a floating object (right).

The work plan as presented in Fig. 4:

Rearrange the position of the data in the matrix,

The desired output (TARGET) coded with ‘1’ for the 1st class (presence of heart disease) and ‘O’ for the second class (absence of heart disease),

Neural network-error,

In order to evaluate the performance of the neural network, we will take a sample that does not belong in the database and we simulate its output by contributing to this network as, we use function sim in this instruction in MATLAB

test-result-vector = sim (network-structure, data)

Matlab simulation application:

testout11 = sim (network11, [2;160;246;0;0;120;1;0;2;3;6;])

Fig. 9.

The characteristic B = f (H) for salt water (left) and salt water with a floating object (right).

Table 1

The labeling of test data set for a 66 year old person

Middle	Time	Distance
Net water + object	1 minute (min) and 35 second (sec)	12.5 cm
Salt water + object	1 min and 5 (sec)	12.5 cm

Table 2

The labeling of training data set

Age	Sexe	Chest pain	BP	Cholesterol	FBS over 120	EKG results	Max HR	Exerice angina	ST depression	Slope of ST	Number of vessels fluro	Thallium	Heart disease
70	1	4	130	322	0	2	109	0	2,4	2	3	3	Presence
57	1	2	124	261	0	0	141	0	0,3	1	0	7	Presence
56	1	3	130	256	1	2	142	1	0,6	2	1	6	Presence
59	1	4	110	239	0	2	142	1	1,2	2	1	7	Presence	Classe 1
60	1	4	140	293	0	2	170	0	1,2	2	2	7	Presence
46	1	4	140	311	0	0	120	1	1,8	2	2	7	Presence
67	1	4	120	229	0	2	129	1	2,6	2	2	7	Presence

64	1	4	128	263	0	0	105	1	0,2	2	1	7	Absence
65	1	4	120	177	0	0	140	0	0,4	1	0	7	Absence
59	1	4	135	234	0	0	161	0	0,5	2	0	7	Absence
53	1	4	142	226	0	0	161	0	0	1	0	7	Absence	Classe 2
44	1	3	140	235	0	2	111	1	0	1	0	3	Absence
64	1	1	110	211	0	2	144	1	1,8	2	0	3	Absence
40	1	1	140	199	0	0	178	1	1,4	1	0	7	Absence

Table 3

Training data set results

	Classe 1							Classe 2
	1	2	3	4	5	6	7	8	9	10	11	12	13	14
Samples	4	2	3	4	4	4	4	4	4	4	4	3	1	1
	130	124	130	110	140	140	120	128	120	135	142	140	110	140
	322	261	256	239	293	311	229	263	177	234	226	235	211	199
	0	0	1	0	0	0	0	0	0	0	0	0	0	0
	2	0	2	2	2	0	2	0	0	0	0	2	2	0
	109	141	142	142	170	120	129	105	140	161	161	111	144	178
	0	0	1	1	0	1	1	1	0	0	0	1	1	1
	2,4	0,3	0,6	1,2	1,2	1,8	2,6	0,2	0,4	0,5	0	0	1,8	1,4
	0	0	0	0	0	0	0	0	0	7	7	3	0	1
	0	0	0	0	0	0	0	0	0	0	0	0	3	0
	0	0	0	0	0	0	0	0	0	0	0	0	0	7
Target	1	1	1	1	1	1	1	0	0	0	0	0	0	0
Output simulation	1	1	1	1	1	1	1	3,8302e-010	−3,904e-011	6,6271e-012	−1,3139e-010	−3,5473e-010	1,4791e-010	6,6254e-012
Neural network-error	5,7535e-010	5,7535e-010	5,7535e-010	6,0831e-010	5,7542e-010	7,4259e-010	5,7575e-010	−3,8302e-010	3,904e-011	−6,6271e-012	1,3139e-010	3,5473e-010	−1,4791e-010	−6,6254e-012

Table 4

The labeling of test data set

Age	Sexe	Chest pain	BP	Cholesterol	FBS over 120	EKG results	Max HR	Exerice angina	ST depression	Slope of ST	Number of vessels fluro	Thallium	Heart disease
59	1	3	126	218	1	0	134	0	2,2	2	1	6	Presence	Classe 1
49	1	3	118	149	0	2	126	0	0,8	1	3	3	Presence
66	1	2	160	246	0	0	120	1	0	2	3	6	Presence
44	1	3	120	226	0	0	169	0	0	1	0	3	Absence	Classe 2
63	1	1	145	233	1	2	150	0	2,3	3	0	6	Absence
70	1	2	156	245	0	2	143	0	0	1	0	3	Absence

Table 5

Test data set results

	Classe 1			Classe 2
	1	2	3	4	5	6
Samples	3	3	2	3	1	2
	126	118	160	120	145	156
	218	149	246	226	233	245
	1	0	0	0	1	0
	0	2	0	0	2	2
	134	126	120	169	150	143
	0	0	1	0	0	0
	2,2	0,8	0	0	2,3	0
	2	1	2	1	3	1
	1	3	3	0	0	0
	6	3	6	3	6	3
Target	1	1	1	0	0	0
Output simulation	0,85072	0,95742	0,86357	4,6211e-005	0,025719	0,011686
Neural network-error	0	0	0	0	0	0

5. Conclusion

Thanks to many kinds of disease-related data available now researchers are constantly attempting to mine useful information out of these; also they can contribute to find the possible solutions.

In this research paper we have worked on a different problem, i.e., towards developing a sensor model based on an electromagnetic waves transmit shock waves through guide wires to specifically cross calcified and fibrotic tissues in the arterial vascular system while leaving intact the elastic wall of healthy vessels the validity of the model was verified by an experiment where a floating mass in a water medium was moved to a certain distance by applying electromagnetic waves.

Experiments studies have proven that the arterial biomarkers have the theoretical advantage that they can integrate the cumulative effect of a traditional risk factor on the arterial wall over a long period of time, so, applying magnetic shocks intermittently to the skin allows to increase the lifespan of the arteries in order to perform their tasks in the best way.

The second aim is to building a predictive model based AI for CAD risk prediction from a structured dataset.

Predicting CAD based on the risk factors is a challenge in itself but the introduction of AI in medicine makes this challenge possible. The use of artificial intelligence techniques is now considered as efficient predictive model.

The results obtained by feed-forward artificial neural network (FFNN) are very impressive. Whereas, the results showed that the simulation error values is of the order of e⁻⁰¹² used to represent the accuracy in predicting of heart failure caused by atherosclerosis independent of risk factors.

Footnotes

Acknowledgement

The authors would like to thank the General Directorate of Scientific Research and Technological Development (DGRSDT) of ALGERIA. This project would not have been possible without it financial support. All thanks and appreciation to the General Directorate of Scientific Research and Technological Development for its financial and moral support, its kind assistance and permanent encouragement.

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