DE112017003670T5 - Method for the diagnosis of noncommunicable diseases based on statistical methods of data processing - Google Patents

Abstract

The invention relates to the field of medicine and is directed to the method for the diagnosis of non-communicable diseases based on statistical methods of data processing in which the parameters characterizing a patient's condition are measured. Recorded patient parameters are processed by means of statistical methods, thereby determining the most significant diagnostic parameters whose deviation from the standard for the presence of a disease speaks. This procedure can be used to diagnose noncommunicable diseases and determine their development in patients.

Description

The invention relates to the field of medicine and is directed to the method for the diagnosis of non-communicable diseases based on statistical methods of data processing in which the parameters characterizing a patient's condition are measured. Recorded patient parameters are processed by means of statistical methods, thereby determining the most significant diagnostic parameters whose deviation from the standard for the presence of a disease speaks. This method can be used to diagnose noncommunicable diseases and determine their development in patients.

State of the art

In diagnosing, because of the variety of parameters that characterize a patient's condition, the selection, classification, and content aggregation of patient parameters to achieve clinical goals is a very difficult task.

In the first diagnostic step, the attending physician, based on his knowledge and intuition, solves the task of classifying all patient data to identify key pathology parameters. It is clear that with such a variety of data and their contexts, solving the problem of data classification is not always appropriate and may not always achieve clinical goals. Medical science greatly facilitates the resolution of population level classification tasks through the application of mathematical methods and models. Known is a method for the diagnosis of noncommunicable diseases based on statistical methods of data processing, in which the parameters characterizing the condition of a patient are measured; recorded patient parameters are processed by statistical methods, thereby determining the most significant diagnostic parameters whose deviation from the standard for the presence of a disease speaks. See invention patent No. 2141247 of 1999, Russian Federation.

This method is particularly close to the present invention in the technical sense and according to the achieved technical result and has been selected as its prototype.

A drawback of this method is considered to be low accuracy of the diagnosis of noncommunicable diseases in a particular patient. This is due to the fact that the diagnostic value of the method in a given patient is based on average data calculated from the present data of many patients in the current state of the art. When determining the cause of the disease, the factors influencing the development of the present disease, which affect the disease not in a specific patient but in the average patient population, are determined. Thus, if the patient is comparable to an average patient according to measured parameters, the diagnosis and the cause of the disease are most likely to be determined fairly accurately. The more the data of the patient differs from that of the average patient, the more the accuracy of the diagnosis is reduced, as well as the accuracy of the causes that have led the patient to such health. This is clearly shown in Table 3 below, which includes the diseases described in the prototype.

Inventive subject matter

The present invention, based on this peculiar observation, aims to propose, in essence, a method of diagnosing noncommunicable diseases based on statistical methods of data processing in which the parameters characterizing a patient's condition are measured. Recorded patient parameters are processed by means of statistical methods, thereby determining the most significant diagnostic parameters whose deviation from the standard for the presence of a disease speaks. This can compensate for at least one of the above-mentioned drawbacks, namely to improve the accuracy of the diagnosis of noncommunicable diseases in a particular patient by identifying the additional factors affecting the development of that disease. This is the technical problem to be solved by the claimed invention.

To achieve this goal, patients' conditional parameters are determined from established anamnestic and clinical, laboratory and instrumental data measured by standards and reference values, by category or by categorical feature of presence. Furthermore, the correlation between all the patient's condition is characteristic Calculated parameters, it is a personal matrix of the clinical and biochemical condition of the patient, which is formed by the probabilities of causing known pathological conditions according to all parameters obtained, created. Thereafter, the obtained personal patient matrix is processed by statistical methods and the most significant diagnostic parameters are determined which affect the condition of the patient and characterize the presence of the disease.

• • die Ausgangsdatenmenge zu erweitern, so können nicht nur Zifferparameter, sondern auch die nach kategorialem Merkmal des Vorhandenseins gemessen Parameter in den statistischen Verfahren der Datenverarbeitung bewertet werden;
• • den Einfluss einiger gemessener Patientenparameter auf andere Patientenparameter zu bestimmen und gleichzeitig Gruppen bezogener Parameter zu identifizieren (dabei ist selbst das Vorhandensein von Zusammenhängen zwischen unabhängigen Parametern die Grundlage für die Analyse von Vorliegen und Ursachen einer Krankheit beim Patienten);
• • von statistischen Verfahren, die auf eine Gruppe ähnlicher Patienten gerichtet sind, zu statistischen Verfahren, die bei einem bestimmten Patienten angewandt werden, zu übergehen. Dadurch können die den Zustand dieses Patienten beeinflussenden Faktoren festgestellt werden.

Thanks to these advantages, it is possible:

• • expand the output data set so that not only digit parameters but also the parameters measured by the categorical feature of the presence can be evaluated in the statistical methods of data processing;
• • To determine the influence of some measured patient parameters on other patient parameters while identifying group-related parameters (even the existence of relationships between independent parameters is the basis for analyzing the presence and causes of a patient's disease);
• • move from statistical methods aimed at a group of similar patients to statistical methods used in a given patient. As a result, the factors influencing the condition of this patient can be determined.

As a result, the treatment of a particular patient is not chosen in accordance with the average statistical recommendations that may not be appropriate for that particular patient, but rather on the basis of personal information. Thus, the factors affecting the patient's illness can be normalized.

In addition, there is a variant of the invention in which the personal patient matrix obtained is processed by means of statistical methods using cluster analysis in order to extract the structural relationships from all possible parameter relationships. Thanks to this favorable feature, it is possible to provide group-related patient parameters, i. such patient parameters within a group, which only interact with each other, and not affect parameters of another group.

There is also a variant of the invention in which the obtained personal patient matrix is processed by statistical methods using factor analysis to determine factor weights of each parameter, their sign and direction of action (positive or negative) under the effect of each of the overall patient parameters. Thanks to this favorable feature, factor weights of each patient parameter, their sign and direction of action can be measured under the influence of each of the entire patient parameters.

There is another variant of the invention in which the obtained personal patient matrix is processed by statistical methods using multiple regression, after which the influence of independent patient parameters on dependent patient parameters is determined. Thanks to this favorable feature, multiple regression can be used to determine the effect of independent patient parameters on dependent patient parameters.

The totality of features essential to the invention is unknown in similar methods according to the state of the art and suggests that the invention be novelty. Moreover, the great importance of this invention and the fact that no such solution has been proposed so far, points to the existence of the inventive step for the proposed solution.

list of figures

• 1 zeigt eine erfindungsgemäße Graph der Zusammenhängen von klinischen, biochemischen und Instrumentendaten zum Beispiel 1.
• 2 zeigt erfindungsgemäße Schritte des Verfahrens zur Diagnose nichtübertragbarer Krankheiten aufgrund statistischer Verfahren der Datenverarbeitung.

Other essential features and advantages of the present invention will be apparent from the non-limiting description of the drawings below, as follows:

• 1 shows a graph according to the invention of the relationships of clinical, biochemical and instrument data, for example 1.
• 2 shows steps of the method according to the invention for the diagnosis of noncommunicable diseases based on statistical methods of data processing.

Solution of the task

The method for diagnosing noncommunicable diseases based on statistical methods of data processing is performed as follows (shown on an example not limiting the application of the invention, as in FIG 1 ).

• • anamnestischen und klinischen Daten,
• • Labor- und Instrumentendaten,

die aufgrund der Normen und Referenzwerten, nach Kategorien oder nach kategorialem Merkmal des Vorhandenseins gemessen werden, festgestellt.Step 1. The parameters characterizing the condition of a patient are obtained from

• • anamnestic and clinical data,
• • laboratory and instrument data,

which are measured on the basis of standards and reference values, by categories or by categorical feature of presence.

As an example, the following 105 parameters can be used, see Table 1 or their representative selection. These examples are from the cohort study in which the patient group was selected according to the Registry Rules [1,2]. Reference values and standards are given according to the existing standards [3-5]. Table 1. Exemplary parameters, their reference values and standards No. feature reference values standard 1 gender Men - 1; Women - 0 2 Older 0 ... 100 3 family history Yes, 1; no- 0 no - 0 4 own family history Yes, 1; no - 0 no - 0 5 arterial hypertension Yes, 1; no - 0 no - 0 6 Vyocardial infarction in the anamnesis Yes, 1; no - 0 no - 0 7 Stroke in the anamnesis Yes, 1; no- 0 no - 0 8th Disorders of carbohydrate metabolism 0 - no 1-glucose tolerance disorder 2 - diabetes mellitus no - 0 9 Smoke Yes, 1; no- 0 no - 0 10 alcohol consumption Yes, 1; no- 0 no - 0 11 Excessive salt consumption Yes, 1; no- 0 no - 0 12 Hypodynamie Yes, 1; no- 0 no - 0 13 psychoemotional stress Yes, 1; no - 0 no - 0 14 Angina pectoris 0 = none 1 = typ. Clinic 2 = atyp. clinic no - 0 15 growth from ... to 16 mass from ... to 17 Body Mass Index (BMI) from ... to <25 18 Heart rate at admission from ... to 60 ... 90 19 systolic blood pressure at admission from ... to <140 20 Diastolic blood pressure at admission from ... to <90 21 blood type 1, 2, 3, 4 22 HGB from ... to Women 110 ... 150 men 130 ... 170 23 RBC from ... to Women 3.5 ... 5.0 Men 4.2 ... 5.6 24 WBC from ... to 4.0-9.0 25 PLT from ... to 180 ... 320 * 10 ⁹ 26 HCT from ... to 33 ... 49 27 BSG from ... to <20 28 glucose from ... to 3.9 ... 6.1 29 uric acid from ... to Women 150 ... 350 men 210 ... 420 30 creatinine from ... to Women 44 ... 97 Men 62 ... 120 31 potassium from ... to 3.5 ... 5.5 32 Creatinine Phosphokinase (CPK) from ... to 10 ... 195 33 Aspartate transferase (AST) from ... to up to 40 U / I 34 Alanine transferase (ALT) from ... to up to 40 U / I 35 Total bilirubin (TBIL) from ... to 8.5 ... 20.5 μmol / L 36 C-reactive protein from ... to to 50 mg / l 37 total cholesterol 1.3 ... 8.0 to 5.0 38 Low density lipoprotein (LDL) 1.5 ... 6.0 to 3.0 39 High density lipoprotein (HDL) 0.7 ... 2.2 to> 1.15 40 Triglycerides (TG) 0.3 ... 3.0 to 1.70 41 Partial thromboplastin time (PTT) from ... to 25 ... 35 s 42 fibrinogen from ... to 2.0 ... 4.0 g / l 43 prothrombin from ... to 78-142% 44 platelet aggregation 0 ... 100 30-60% 45 Thrombozytendisaggregation from ... to 46 Changes in the ECG at admission Yes, 1; no - 0 no - 0 47 Arrhythmias in the ECG at admission Yes, 1; no - 0 no - 0 0 = no; 1 = intraventricular conduction disorder 48 Thigh block on the ECG during recording 2 = left anterior hemiblock 3 = incomplete LSB 4 = incomplete RSB 5 = complete RSB 49 pathological Q wave in the ECG at admission Yes, 1; no - 0 no - 0 paroxysmal tachycardia on ECG at admission 0 - none 50 1- ST reduction no - 0 2 - negative T-wave 51 Signs of left ventricular hypertrophy on ECG at admission Yes, 1; no - 0; no - 0 52 QT interval from ... to 53 contractility Yes, 1; no - 0; no - 0 54 left ventricular ejection fraction (LVEF) from ... to > 60% 55 Orifice of the aorta from ... to <3,8 cm 56 left ventricular end-diastolic diameter (LVEDD) from ... to up to 5,6 cm 57 end-diastolic diameter of the left atrium from ... to 58 End-systolic diameter of the left atrium from ... to to 4.0 cm 59 Thickness of the interventricular septum from ... to 0.7 ... 1.1 60 left ventricular posterior wall thickness from ... to 0.7 ... 1.1 61 Arteriosclerosis of the aorta after echocardiogram yes - 1, no - 0 62 Heart rhythm in the long-term ECG 1- sinus rhythm 1 - sinus rhythm 2 - atrial fibrillation 63 average heart rate from ... to 64 asymptomatic ischemia episodes Yes, 1; no - 0 no - 0 65 symptomatic ischemic episodes Yes, 1; no - 0 no - 0 66 Number of ventricular extrasystoles (VES) from ... to until 100 67 Number of supraventricular extrasystoles (SVES) Yes, 1; no - 0 68 paroxysmal atrial fibrillation Yes, 1; no - 0 no - 0 69 ventricular paroxysmal tachycardia Yes, 1; no - 0 no - 0 70 Breaks over 2 seconds Yes, 1; no - 0 no - 0 71 Is a Stress Tolerance Test (ETT) feasible? Yes, 1; no - 0 no - 0 72 If not, then for what reason? from 0 (already performed) to 8 (no consent of the patient) 73 Information value of ETT Yes, 1; no - 0 74 Duration of ETT from ... to 75 maximum heart rate at ETT from ... to 76 Max. systolic arterial blood pressure (SAD) at ETT from ... to 77 Arrhythmia in ETT Yes, 1; no - 0 78 Is the desired heart rate at the ETT reached? Yes, 1; no - 0 79 Stenocardia clinic at the ETT Yes, 1; no - 0 80 Disruption reason of ETT 0 = submax. heart rate 1 = stenocardia 2 = massive ST lowering 3 = stenocardia + ST depression 4 = other 81 Max. ST-lowering at the ETT from ... to 82 0 - negative ETT interpretation 1 - positive 3 - doubtful 83 Duke Treadmill Score (DTS) from ... to ≥5 - low risk of - 10 to 5 - medium risk <10 - high risk 84 Carotid atherosclerosis in the duplex examination after IMD ≥1.3 mm Yes, 1; no - 0 no - 0 85 Carotid atherosclerosis in the duplex examination to IMD ≥10 mm Yes, 1; no - 0 no - 0 86 Intima Media Thickness (IMD) of the ACC from ... to 87 Max. IMD the ACC from ... to 88 Max. Stenosis degree in the ACC and / or ACI from ... to no - 0 89 Max. linear blood flow velocity in the lesion site from ... to 90 Changes in the fundus during the examination by the ophthalmologist Yes, 1; no - 0 no - 0 91 Narrowing of coronary arteries (CIHK) in stenosis ≥ 50% Yes, 1; no - 0 no - 0 92 Type (grade) of coronary artery disease 0 - none 1-1-vascular disease no - 0 3 - 2,3-vascular disease 93 Max. Stenosis grade in coronary arteries from ... to 94 Stenosis grade: Main trunk of the left coronary artery (LCA) 0 to 100.0% 95 Stenosis grade: left anterior descending branch (LAD) 0 to 100.0% 96 Stenosis grade: left circumflex artery (CX) 0 to 100.0% 97 Stenosis grade: Ramus diagonalis (RD) 0 to 100.0% 98 Stenosis grade: Ramus Marginalis Sinister (RMS) 0 to 100.0% 99 Stenosis grade: right. Coronary artery (RCA) 0 to 100.0% 100 Stenosis grade: posterolateral ramus (RPL) 0 to 100.0% 101 Stenosis grade: Ramus interventricularis posterior (RIVP) 0 to 100.0% 102 Changes (hypertonicity) in REG Yes, 1; no - 0 no - 0 103 pathological reaction during stress ECG while walking (TeleEKG) Yes, 1; no - 0 no - 0 104 8 - low risk Morise pretest score 9 ... 15 - medium risk > 15 - high risk 105 0 ... 39 - low risk Morise Exercise Test Score 40 ... 60 - medium risk > 60 - high risk

The above parameter list is not complete and is only an example. Ultimately, the number of parameters of pathological conditions of the patient depends on the progress in medical technology. Thus, for example, details of the patient's promiotic studies can be included in the list of pathology features listed above.

In addition, these parameters can be supplemented by the parameters describing a patient's way of life; this allows the influence of the last to be determined together with deviations of the parameters determined by diagnostic methods.

Each of the above features (like any other) has a specific calculated probability factor. In this case, a probability value has both quantitative and qualitative features that describe the condition as well as a patient, as well as the population within a precisely defined quantitative framework. This conclusion is important because the very methods of analyzing high-resolution quantitative features are most advanced in various fields of mathematics.

1. 1. Ja oder nein (1, wenn Daten zu diesem Parameter vorhanden sind, und 0, falls nicht)
2. 2. Im Normbereich (1, falls ja, und 0, falls nicht)
3. 3. Außer der Norm (1, falls ja, und 0, falls nicht)
4. 4. Über der Norm (1, falls ja, und 0, falls nicht)
5. 5. Unter der Norm (1, falls ja, und 0, falls nicht)
6. 6. Durchschnitt
7. 7. Über Durchschnitt (1, falls ja, und 0, falls nicht)
8. 8. Unter Durchschnitt (1, falls ja, und 0, falls nicht)
9. 9. 0-3% (1, wenn der Variationskoeffizient zu diesem Intervall passt, 0, wenn nicht)
10. 10. 3.01-10% (das gleiche)
11. 11. 10.01-20% (das gleiche)
12. 12. 20.01-30% (das gleiche)
13. 13. Mehr als 30,01% (das gleiche).

Step 2. For the parameters with reference values, a reference value change of the specified parameter is made in the direction of the measured value of the patient parameter, thus the personal reference value of the specified parameter is determined for the patient and the remaining parameter is assigned the value by categorical feature of the presence. Thereafter, a personal matrix is created with clinical and biochemical data of the patient formed by binary coefficients. The binary coefficients correspond to the comparison of each parameter with a personal reference value and each parameter within an interval of deviations of the measured value from the personal reference value of the specified parameter. The parameters characterizing the condition of the patient are sequentially stored in the columns of the matrix, and the norms and different deviation intervals of the measured value from the personal reference value of the specified parameter are arranged in the rows of the matrix. The binary coefficient is assigned 1 if the parameter value is within the specified interval, and 0 if not. Here are examples of specific intervals:

1. 1. Yes or no (1 if there is data for this parameter, and 0 if not)
2. 2. In the normal range (1, if yes, and 0, if not)
3. 3. Except the norm (1, if yes, and 0, if not)
4. 4. Above the norm (1, if yes, and 0, if not)
5. 5. Under the norm (1, if yes, and 0, if not)
6. 6th average
7. 7. Over average (1, if yes, and 0 if not)
8. 8. Below average (1, if yes, and 0 if not)
9. 9. 0-3% (1 if the coefficient of variation matches this interval, 0 if not)
10. 10. 3.01-10% (the same)
11. 11. 10.01-20% (the same)
12. 12. 20.01-30% (the same)
13. 13. More than 30.01% (the same).

Step 3. The personal patient matrix is processed by statistical methods, which determines the structure of the parameters and their interdependence, which determines the development of the disease in a patient.

Step 3.1. The resulting personal patient matrix can be manipulated by statistical methods using cluster analysis to extract the structural relationships from all possible parameter relationships. Step 3.2. The resulting personal patient matrix may be manipulated by statistical methods using factor analysis to determine factor weights of each parameter, their sign and direction of action (positive or negative) under the effect of each of the overall patient parameters.
Step 3.3. The resulting personal patient matrix can be manipulated using statistical techniques using multiple regression, which determines the influence of independent patient parameters on dependent patient parameters.
Step 4. By processing the steps mentioned above, the most significant diagnostic parameters are determined whose deviation from the norm indicates the presence of the disease.

The above embodiments of the invention are exemplary and allow to add new variants or to modify those already described. Thus, e.g. The above-described statistical methods for data processing of a patient are used because it is not the application of certain statistical data that is significant, but the goal - for which this data is used.

The totality of more personal matrices of the clinical and biochemical condition of each patient can be converted into a general population matrix, since both a patient and the whole population have the same qualitative and quantitative status of clinical and biochemical pathological features.

Each of the parameters having specific values of the calculated likelihood factors may be represented in a personal patient matrix, in a general population matrix in the form of a dependent or independent variable. Depending on the task, the matrices can be processed using various mathematical analysis methods.

Industrial Applicability

The present method for diagnosing noncommunicable diseases based on statistical data processing techniques can be practiced by those skilled in the art and ensures the realization of registered applications. This suggests the industrial applicability of the invention.

The described method of diagnosing noncommunicable diseases due to statistical methods of data processing is enforced due to conventional technologies, and the possibility of its enforcement entails no additional technical problems.

• - Berechnungen für die erste Gruppe mit 86 Patienten aus einer Kohortenstudie, die der Korrelation von Karotis- und Koronaratherosklerose bei Patienten mit Herz-Kreislauf-Erkrankungen gewidmet wurde [1,2];
• - Berechnungen für die zweite Gruppe mit 12 Patienten mit kardiologischen und urologischen Erkrankungen, die aufgrund strenger Einschlusskriterien aus dem Register der Abteilung für allgemeine Kardiologie der FGBU OBP Moskaus für 2012 (abgekürzt: ROK-2012) ausgewählt wurde und die Kohorte der Forschungsstudie „Cardioprognoz“ (CUP) gebildet hat.

According to the proposed invention, the applicant has performed calculations according to this method as follows:

• - Calculations for the first group of 86 patients from a cohort study devoted to the correlation of carotid and coronary atherosclerosis in patients with cardiovascular disease [1,2];
• - Calculations for the second group of 12 patients with cardiological and urological diseases, selected on the basis of strict inclusion criteria from the register of the General Cardiology Unit of the FGBU OBP Moscow for 2012 (ROK-2012) and the cohort of the research study "Cardioprognoz" (CUP) has formed.

In Table 3 below, the data modified by mathematical-statistical methods of 105 parameters are collected for different patient groups. The patient groups were selected from the first group by cluster analysis according to 105 parameters (see step 1).

• Gruppe 1 mit 86 Patienten ist in Bezug auf die folgenden Gruppen eine Gesamtheit.
• Gruppe 2 hat 33 Patienten,
• Gruppe 3 - 23 Patienten,
• Gruppe 4 - 19 Patienten,
• Gruppe 5 - 11 Patienten.

Tabelle 3 zeigt die berechneten Gruppendaten nach Durchschnittswerten und Faktorlasten für jede der oben genannten Gruppen. Tabelle 3. Berechnete Gruppendaten nach Durchschnittswerten (DW) und Faktorlasten (FL) Nr DW 86 DW 33 DW 23 DW 19 DW 10 FL 86 FL 33 FL 23 FL 19 FL 10 1 0.86 0.83 0.88 0.84 0.64 -0.02 0.04 0.11 0.28 0.37 2 57 55.45 55.84 57.47 60.73 0.54 0.52 -0.5 0.33 -0.2 3 0.36 0.39 0.29 0.42 0.27 0.04 0.39 -0.11 0.46 0.08 4 0.71 0.64 0.77 0.74 0.73 0.25 0.2 -0.34 0.35 -0.09 5 0.78 0.79 0.73 0.89 0.73 -0.16 -0.02 0.08 -0.34 -0.54 6 0.28 0.18 0.46 0.11 0.54 0.45 0.43 -0.55 0.19 0.4 7 0.03 0.03 0.01 0.05 0.09 0.3 0.14 -0.31 0.25 0.43 8 0.44 0.37 0.58 0.42 0.63 0.29 0.3 -0.28 0.17 0.31 9 0.42 0.33 0.46 0.47 0.54 0.26 0.33 0.46 0.57 0.72 10 0.42 0.45 0.42 0.53 0.27 -0.13 0.45 0.24 0.01 -0.26 11 0.5 0.52 0.54 0.47 0.54 0.09 0.44 0.09 0.14 -0.12 12 0.51 0.58 0.46 0.47 0.45 -0.05 0.31 0.14 -0.17 -0.29 13 0.64 0.61 0.58 0.79 0.45 0.01 -0.03 0.1 -0.06 0.51 14 1.21 1.24 1.31 1.21 1.27 -0.32 -0.06 0.53 -0.28 -0.48 15 1.76 1.75 1.78 1.78 1.71 -0.20 0.14 0.25 -0.16 -0.3 16 89.7 90.09 94.38 86.26 82.18 -0.27 0.42 0.59 -0.47 -0.50 17 29.29 29.64 20.53 28.36 27.58 -0.28 0.29 0.49 -0.62 -0.45 18 69.94 68.91 71.46 71.21 68.72 -0.08 -0.92 0.15 -0.16 0.3 19 138.83 137.58 144.23 140.47 134.36 -0.13 0.11 0.15 -0.19 -0.58 20 85.58 86.06 87.69 86.42 78.18 -0.31 0.04 0.42 -0.46 -0.61 21 2.06 2.06 2 2.05 2.09 0.001 0.13 0.14 -0.02 0,14 22 146.99 147.48 147.54 146.21 141 -0.37 0.01 0.54 -0.37 0.59 23 4.75 4.82 4.74 4.64 4.66 -0.4 -0.05 0.59 -0.22 0.15 24 6.2 6.11 6.14 6.07 7.14 -0.1 -0.03 0.41 0.06 0.41 25 222.84 226.54 222.69 219.58 218.27 0.12 -0.22 0.22 0.2 -0.21 26 43.67 44.39 43.32 43.16 41.7 -0.3 0.05 0.43 -0.15 0.34 27 10.57 7.56 14.77 11.58 7.64 0.27 0.05 -0.46 0.09 -0.38 28 6.07 5.89 6.26 5.8 6.74 0.23 0.3 -0.18 -0.14 0.37 29 370.64 359.2 379.85 407.11 331.91 -0.04 0.07 -0.21 0.16 -0.29 30 94.83 93.03 97.62 97.16 85.36 0.31 0.4 -0.31 0.4 0.37 31 4.43 4.43 4.48 4.36 4.5 0.08 0.16 0.2 0.3 -0.66 32 180.93 118.65 225.49 178.06 274.97 0.27 -0.27 0.3 0.62 0.54 33 27.34 27.12 28.55 29.3 21.81 0.07 -0.32 0.18 0.31 0.29 34 37.45 39.28 39.79 38.38 23.33 -0.26 -0.29 0.32 0.19 -0.28 35 16.4 16.59 14.61 18.76 14.94 0.12 0.44 -0.24 -0.24 0.58 36 0.09 0.11 0.07 0.08 0.13 -0.29 -0.16 0.48 -0.11 -0.53 37 229.09 223.58 220.05 251.37 222.27 0.04 -0.14 0.11 0.37 -0.19 38 141.68 142.23 140.27 141.96 140.14 0.04 -0.04 -0.12 0.28 0.35 39 47.94 48.5 47.1 50.11 44.6 -0.05 -0.05 -0.02 -0.04 -0.65 40 196.05 156.41 235.15 206.05 187.68 -0.04 -0.12 0.17 -0.08 -0.14 41 34.12 34.79 34.56 32.48 33.38 0.02 -0.05 -0.15 0.01 0.36 42 365.08 351.64 417.91 367.36 371.04 0.18 0.13 -0.12 0.01 -0.37 43 102.52 104.87 100.76 101.37 104.23 -0.11 -0.08 0.01 0.02 -0.12 44 51.67 51.47 50.64 51.68 52.73 0.11 0.23 0.34 0.39 0.22 45 33.54 34.74 29.41 38.01 30.68 -0.01 -0.09 -0.03 0.2 -0.31 46 0.71 0.64 0.69 0.84 0.81 0.12 -0.08 -0.21 0.35 -0.26 47 0.1 0.03 0.15 0.21 0.09 0.13 -0.14 -0.22 0.17 -0.01 48 1.09 1.1 1.15 1.16 0.82 -0.08 -0.43 -0.27 -0.13 0.13 49 0.1 0.03 0.1 0.11 0.27 0.34 0.49 -0.37 0.24 0.26 50 0.87 0.58 0.96 1.05 1.09 0.22 0.11 -0.3 -0.01 0.57 51 0.14 0.18 0.04 0.21 0.18 0.04 0.12 -0.11 -0.07 -0.62 52 0.41 0.41 0.41 0.42 0.41 0.04 0.32 0.44 0.15 0.12 53 0.29 0.21 0.38 0.21 0.04 0.29 0.18 -0.5 -0.12 -0.01 54 64.02 68.85 59 63.11 59.36 -0.39 -0.29 0.49 -0.04 -0.51 55 3,69 3.73 3.66 3.87 3.69 -0.03 0.29 0.35 -0.01 0.32 56 5.54 5.38 5.73 5.62 5.3 0.07 0.17 -0.05 -0.19 0.03 57 4.11 4 4.2 4.18 3.98 0.26 0.34 -0.44 0.02 -0.44 58 74.65 68.45 84.46 72.63 0.01 0.21 0.41 -0.14 -0.06 0.01 59 1.12 1.15 1.1 1.11 1.08 -0.05 0.25 -0.05 -0.36 -0.73 60 1.13 1.13 1.11 1.13 1.13 0.06 0.43 -0.25 -0.48 -0.38 61 0.74 0.67 0.81 0.52 0.09 0.51 0.39 -0.39 0.52 0.37 62 0.9 0.97 0.85 0.84 0.91 -0.16 -0.06 0.09 0.13 -0.1 63 67.71 65.7 67.96 71.84 69.27 -0.03 -0.21 -0.02 -0.33 0.48 64 0.26 0.21 0.15 0.37 0.18 0.26 0.13 0.11 0.66 0.1 65 0.23 0.15 0.23 0.37 0.09 0.43 0.48 -0.34 0.21 0.07 66 319.22 15.63 92.43 280.26 139.1 -0.08 -0.28 -0.12 -0.49 -0.09 67 234.15 30.61 45.88 930.37 31.45 0.2 -0.16 0.36 0.41 0.58 68 0.09 0.03 0.1 0.26 0.03 0.05 -0.14 0.01 -0.01 -0.01 69 0.19 0.21 0.19 0.16 0.09 -0.01 0.25 -0.08 -0.34 0.07 70 0.1 0.03 0.19 0.16 0.01 0.18 0.14 0.02 0.18 -0.12 71 0.6 0.67 0.54 0.53 0.64 -0.27 -0.32 0.78 -0.05 0.24 72 1.2 1.21 1.62 1.16 1.26 -0.02 -0.03 -0.03 -0.32 -0.52 73 0.64 0.67 0.64 0.58 0.36 0.01 -0.02 -0.25 -0.38 0.49 74 6.4 7.69 7.08 6.88 5.95 -0.32 -0.22 0.31 -0.12 -0.07 75 121.26 122.2 116.25 126.16 114.9 -0.22 -0.54 -0.16 -0.49 0.07 76 174.65 173.12 172.81 181.74 167.27 -0.04 -0.03 0.08 0.23 -0.19 77 0.36 0.27 0.38 0.53 0.09 0.14 0.12 0.41 0.53 0.43 78 0.34 0.42 0.27 0.42 0.26 -0.14 -0.06 -0.15 -0.33 -0.3 79 0.46 0.48 0.42 0.58 0.27 0.2 0.22 -0.03 0.57 -0.02 80 2.31 2.12 2.23 2.53 2.82 0.15 0.41 0.17 -0.06 -0.36 81 1 1.18 1.04 0.66 0.82 -0.1 0.04 0.15 0.11 -0.42 82 1.16 1.18 1.08 1.21 1.36 0.1 0.25 -0.16 -0.23 -0.51 83 1,57 1,53 3,44 1,03 1,06 -0,04 -0,21 -0,31 -0,14 -0,52 84 0,69 0,58 0,65 0,84 0,73 0,7 0,62 -0,67 0,67 0,07 85 0,77 0,76 0,73 0,74 0,82 0,54 0,4 -0,43 0,66 -0,18 86 1,22 1,18 1,21 1,28 1,23 0,63 0,72 -0,56 0,56 0,2 87 1,28 1,28 1,25 1,33 1,28 0,51 0,59 -0,53 0,45 -0,23 88 18,56 13,67 15,38 24,95 30,27 0,59 0,37 -0,57 0,61 0,46 89 1,41 1,74 1,12 2,01 1,67 0,1 -0,07 0,15 0,19 0,47 90 0,7 0,73 0,69 0,63 0,64 0,27 0,24 -0,39 0,19 0,15 91 0,6 0,39 0,73 0,63 0,82 0,82 0,83 -0,71 0,89 0,81 92 1,42 0,79 1,73 1,58 2,18 0,86 0,84 -0,77 0,91 0,81 93 58,1 40,73 70,64 57,36 77,45 0,82 0,8 0,71 0,93 0,86 94 6,24 1,06 11,81 3,17 8,64 0,32 0,18 -0,28 0,25 0,24 95 42,85 24,82 56,15 42,43 63,64 0,69 0,54 -0,53 0,8 0,72 96 29,36 15,45 36,42 35,42 38,18 0,7 0,68 -0,63 0,7 0,6 97 21,3 11,36 26,31 24,47 29,55 0,54 0,61 -0,43 0,63 0,53 98 15,34 10,24 17,46 11,68 18,69 0,49 0,56 -0,47 0,32 0,19 99 41,64 14,06 38,16 41,57 61,09 0,66 0,37 -0,64 0,81 0,71 100 12,14 8,94 9,04 12,63 8,64 0,35 0,51 -0,23 0,48 0,45 101 2,94 0,01 4,04 7,73 0,01 0,16 -0,37 0,05 0,38 0,25 102 0,66 0,7 0,69 0,58 0,91 0,17 0,7 0,07 0,38 -0,25 103 0,51 0,61 0,58 0,37 0,45 0,01 -0,19 -0,36 0,53 0,06 104 14,48 14 15,27 14,74 15,91 0,56 0,57 -0,5 0,46 0,36 105 57,22 56,01 61,34 54,68 56,18 0,12 0,49 0,17 0,27 -0,2 In total, five groups of patients were formed by cluster analysis:

• Group 1 with 86 patients is a whole with respect to the following groups.
• Group 2 has 33 patients
• Group 3 - 23 patients,
• Group 4 - 19 patients,
• Group 5 - 11 patients.

Table 3 shows the calculated group data by means of averages and factor loads for each of the above groups. Table 3. Calculated group data by average values (DW) and factor loads (FL) No DW 86 DW 33 DW 23 DW 19 DW 10 FL 86 FL 33 FL 23 FL 19 FL 10 1 0.86 0.83 0.88 0.84 0.64 -0.02 12:04 12:11 12:28 12:37 2 57 55.45 55.84 57.47 60.73 12:54 12:52 -0.5 12:33 -0.2 3 12:36 12:39 12:29 12:42 12:27 12:04 12:39 -0.11 12:46 12:08 4 0.71 0.64 0.77 0.74 0.73 12:25 0.2 -0.34 12:35 -0.09 5 0.78 0.79 0.73 0.89 0.73 -0.16 -0.02 12:08 -0.34 -0.54 6 12:28 12:18 12:46 12:11 12:54 12:45 12:43 -0.55 12:19 0.4 7 12:03 12:03 12:01 12:05 12:09 0.3 12:14 -0.31 12:25 12:43 8th 12:44 12:37 12:58 12:42 0.63 12:29 0.3 -0.28 12:17 12:31 9 12:42 12:33 12:46 12:47 12:54 12:26 12:33 12:46 12:57 0.72 10 12:42 12:45 12:42 12:53 12:27 -0.13 12:45 12:24 12:01 -0.26 11 0.5 12:52 12:54 12:47 12:54 12:09 12:44 12:09 12:14 -0.12 12 12:51 12:58 12:46 12:47 12:45 -0.05 12:31 12:14 -0.17 -0.29 13 0.64 0.61 12:58 0.79 12:45 12:01 -0.03 0.1 -0.06 12:51 14 1.21 1.24 1.31 1.21 1.27 -0.32 -0.06 12:53 -0.28 -0.48 15 1.76 1.75 1.78 1.78 1.71 -0.20 12:14 12:25 -0.16 -0.3 16 89.7 90.09 94.38 86.26 82.18 -0.27 12:42 12:59 -0.47 -0.50 17 29.29 29.64 20:53 28.36 27.58 -0.28 12:29 12:49 -0.62 -0.45 18 69.94 68.91 71.46 71.21 68.72 -0.08 -0.92 12:15 -0.16 0.3 19 138.83 137.58 144.23 140.47 134.36 -0.13 12:11 12:15 -0.19 -0.58 20 85.58 86.06 87.69 86.42 78.18 -0.31 12:04 12:42 -0.46 -0.61 21 2:06 2:06 2 2:05 2:09 0001 12:13 12:14 -0.02 0.14 22 146.99 147.48 147.54 146.21 141 -0.37 12:01 12:54 -0.37 12:59 23 4.75 4.82 4.74 4.64 4.66 -0.4 -0.05 12:59 -0.22 12:15 24 6.2 6.11 6.14 6:07 7.14 -0.1 -0.03 12:41 12:06 12:41 25 222.84 226.54 222.69 219.58 218.27 12:12 -0.22 12:22 0.2 -0.21 26 43.67 44.39 43.32 43.16 41.7 -0.3 12:05 12:43 -0.15 12:34 27 10:57 7:56 14.77 11:58 7.64 12:27 12:05 -0.46 12:09 -0.38 28 6:07 5.89 6.26 5.8 6.74 12:23 0.3 -0.18 -0.14 12:37 29 370.64 359.2 379.85 407.11 331.91 -0.04 12:07 -0.21 12:16 -0.29 30 94.83 93.03 97.62 97.16 85.36 12:31 0.4 -0.31 0.4 12:37 31 4:43 4:43 4:48 4:36 4.5 12:08 12:16 0.2 0.3 -0.66 32 180.93 118.65 225.49 178.06 274.97 12:27 -0.27 0.3 0.62 12:54 33 27.34 27.12 28.55 29.3 21.81 12:07 -0.32 12:18 12:31 12:29 34 37.45 39.28 39.79 38.38 23:33 -0.26 -0.29 12:32 12:19 -0.28 35 16.4 16:59 14.61 18.76 14.94 12:12 12:44 -0.24 -0.24 12:58 36 12:09 12:11 12:07 12:08 12:13 -0.29 -0.16 12:48 -0.11 -0.53 37 229.09 223.58 220.05 251.37 222.27 12:04 -0.14 12:11 12:37 -0.19 38 141.68 142.23 140.27 141.96 140.14 12:04 -0.04 -0.12 12:28 12:35 39 47.94 48.5 47.1 50.11 44.6 -0.05 -0.05 -0.02 -0.04 -0.65 40 196.05 156.41 235.15 206.05 187.68 -0.04 -0.12 12:17 -0.08 -0.14 41 34.12 34.79 34.56 32.48 33.38 12:02 -0.05 -0.15 12:01 12:36 42 365.08 351.64 417.91 367.36 371.04 12:18 12:13 -0.12 12:01 -0.37 43 102.52 104.87 100.76 101.37 104.23 -0.11 -0.08 12:01 12:02 -0.12 44 51.67 51.47 50.64 51.68 52.73 12:11 12:23 12:34 12:39 12:22 45 33.54 34.74 29.41 38.01 30.68 -0.01 -0.09 -0.03 0.2 -0.31 46 0.71 0.64 0.69 0.84 0.81 12:12 -0.08 -0.21 12:35 -0.26 47 0.1 12:03 12:15 12:21 12:09 12:13 -0.14 -0.22 12:17 -0.01 48 1:09 1.1 1.15 1.16 0.82 -0.08 -0.43 -0.27 -0.13 12:13 49 0.1 12:03 0.1 12:11 12:27 12:34 12:49 -0.37 12:24 12:26 50 0.87 12:58 0.96 1:05 1:09 12:22 12:11 -0.3 -0.01 12:57 51 12:14 12:18 12:04 12:21 12:18 12:04 12:12 -0.11 -0.07 -0.62 52 12:41 12:41 12:41 12:42 12:41 12:04 12:32 12:44 12:15 12:12 53 12:29 12:21 12:38 12:21 12:04 12:29 12:18 -0.5 -0.12 -0.01 54 64.02 68.85 59 63.11 59.36 -0.39 -0.29 12:49 -0.04 -0.51 55 3.69 3.73 3.66 3.87 3.69 -0.03 12:29 12:35 -0.01 12:32 56 5:54 5:38 5.73 5.62 5.3 12:07 12:17 -0.05 -0.19 12:03 57 4.11 4 4.2 4.18 3.98 12:26 12:34 -0.44 12:02 -0.44 58 74.65 68.45 84.46 72.63 12:01 12:21 12:41 -0.14 -0.06 12:01 59 1.12 1.15 1.1 1.11 1:08 -0.05 12:25 -0.05 -0.36 -0.73 60 1.13 1.13 1.11 1.13 1.13 12:06 12:43 -0.25 -0.48 -0.38 61 0.74 0.67 0.81 12:52 12:09 12:51 12:39 -0.39 12:52 12:37 62 0.9 0.97 0.85 0.84 0.91 -0.16 -0.06 12:09 12:13 -0.1 63 67.71 65.7 67.96 71.84 69.27 -0.03 -0.21 -0.02 -0.33 12:48 64 12:26 12:21 12:15 12:37 12:18 12:26 12:13 12:11 0.66 0.1 65 12:23 12:15 12:23 12:37 12:09 12:43 12:48 -0.34 12:21 12:07 66 319.22 15.63 92.43 280.26 139.1 -0.08 -0.28 -0.12 -0.49 -0.09 67 234.15 30.61 45.88 930.37 31.45 0.2 -0.16 12:36 12:41 12:58 68 12:09 12:03 0.1 12:26 12:03 12:05 -0.14 12:01 -0.01 -0.01 69 12:19 12:21 12:19 12:16 12:09 -0.01 12:25 -0.08 -0.34 12:07 70 0.1 12:03 12:19 12:16 12:01 12:18 12:14 12:02 12:18 -0.12 71 0.6 0.67 12:54 12:53 0.64 -0.27 -0.32 0.78 -0.05 12:24 72 1.2 1.21 1.62 1.16 1.26 -0.02 -0.03 -0.03 -0.32 -0.52 73 0.64 0.67 0.64 12:58 12:36 12:01 -0.02 -0.25 -0.38 12:49 74 6.4 7.69 7:08 6.88 5.95 -0.32 -0.22 12:31 -0.12 -0.07 75 121.26 122.2 116.25 126.16 114.9 -0.22 -0.54 -0.16 -0.49 12:07 76 174.65 173.12 172.81 181.74 167.27 -0.04 -0.03 12:08 12:23 -0.19 77 12:36 12:27 12:38 12:53 12:09 12:14 12:12 12:41 12:53 12:43 78 12:34 12:42 12:27 12:42 12:26 -0.14 -0.06 -0.15 -0.33 -0.3 79 12:46 12:48 12:42 12:58 12:27 0.2 12:22 -0.03 12:57 -0.02 80 2.31 2.12 2.23 2:53 2.82 12:15 12:41 12:17 -0.06 -0.36 81 1 1.18 1:04 0.66 0.82 -0.1 12:04 12:15 12:11 -0.42 82 1.16 1.18 1:08 1.21 1:36 0.1 12:25 -0.16 -0.23 -0.51 83 1.57 1.53 3.44 1.03 1.06 -0.04 -0.21 -0.31 -0.14 -0.52 84 0.69 0.58 0.65 0.84 0.73 0.7 0.62 -0.67 0.67 0.07 85 0.77 0.76 0.73 0.74 0.82 0.54 0.4 -0.43 0.66 -0.18 86 1.22 1.18 1.21 1.28 1.23 0.63 0.72 -0.56 0.56 0.2 87 1.28 1.28 1.25 1.33 1.28 0.51 0.59 -0.53 0.45 -0.23 88 18.56 13.67 15.38 24,95 30.27 0.59 0.37 -0.57 0.61 0.46 89 1.41 1.74 1.12 2.01 1.67 0.1 -0.07 0.15 0.19 0.47 90 0.7 0.73 0.69 0.63 0.64 0.27 0.24 -0.39 0.19 0.15 91 0.6 0.39 0.73 0.63 0.82 0.82 0.83 -0.71 0.89 0.81 92 1.42 0.79 1.73 1.58 2.18 0.86 0.84 -0.77 0.91 0.81 93 58.1 40.73 70.64 57.36 77.45 0.82 0.8 0.71 0.93 0.86 94 6.24 1.06 11.81 3.17 8.64 0.32 0.18 -0.28 0.25 0.24 95 42.85 24.82 56.15 42.43 63.64 0.69 0.54 -0.53 0.8 0.72 96 29.36 15,45 36.42 35.42 38.18 0.7 0.68 -0.63 0.7 0.6 97 21.3 11.36 26.31 24.47 29.55 0.54 0.61 -0.43 0.63 0.53 98 15.34 10.24 17.46 11.68 18.69 0.49 0.56 -0.47 0.32 0.19 99 41.64 14.06 38.16 41.57 61.09 0.66 0.37 -0.64 0.81 0.71 100 12.14 8.94 9.04 12,63 8.64 0.35 0.51 -0.23 0.48 0.45 101 2.94 0.01 4.04 7.73 0.01 0.16 -0.37 0.05 0.38 0.25 102 0.66 0.7 0.69 0.58 0.91 0.17 0.7 0.07 0.38 -0.25 103 0.51 0.61 0.58 0.37 0.45 0.01 -0.19 -0.36 0.53 0.06 104 14.48 14 15.27 14,74 15.91 0.56 0.57 -0.5 0.46 0.36 105 57.22 56.01 61.34 54.68 56.18 0.12 0.49 0.17 0.27 -0.2

Column 1 of Table 3 lists the parameter numbers (see step 1), columns 2-6 show average values of the parameters for groups of 86, 33, 23, 19 and 11 patients. Columns 7 to 11 give numerical values of the factor loads calculated by principal component analysis.

1. 1. 34:42;
2. 2. 100:101;
3. 3. 90:46;
4. 4. 22:18;
5. 5. 41:47;
6. 6. 26:30;
7. 7. 57:58;
8. 8. 57:59;
9. 9. 25:33.

As can be seen from the table, most averages are very similar to the total list of 105 parameters in each of the five groups by quantitative measurements of patients. In other words, each data selection reflects linear values of most collected parameters from the total of 86 patients. The situation with factor loads, which reflect the systemic context of each parameter, is quite different. Of all five groups of patients, significant values of factor loadings (> 0.72) are only present in parameter 93 (maximum stenosis grade in coronary arteries). Absolute amount of factor loads of most parameters vary from -0.62 to + 0.93. With relatively constant arithmetic mean for more than two-thirds of medical parameters, the high volatility of factor loads demonstrates a significant likelihood of detecting a significant amount of relationships between existing parameters of cardiopathology. In all five patient groups, a consistent system of relationships was found from nine pairs of interrelated features:

1. 1. 34:42;
2. 2. 100: 101;
3. 3. 90:46;
4. 4. 22:18;
5. 5. 41:47;
6. 6. 26:30;
7. 7. 57:58;
8. 8. 57:59;
9. 9. 25:33.

• • stellen zwei Paare Zusammenhänge zwischen klinischen Blutwerten (Paare 25:33, 26:30) dar,
• • zeigen zwei Paare Zusammenhänge zwischen den Parametern der biochemischen Blutanalyse (Paare 41:47, 34:42),
• • andere fünf Paare zeigen Zusammenhänge zwischen den mittels instrumentelles Verfahrens erhaltenen Parametern,
• • Sonstige miteinander verbundene Paare von Herzparametern unterscheiden sich in allen fünf Patientengruppen stark.

From this system of uniform connections

• • two pairs represent correlations between clinical blood values (pairs 25:33, 26:30),
• • two pairs show correlations between the parameters of the biochemical blood analysis (pairs 41:47, 34:42),
• • other five pairs show correlations between the parameters obtained by instrumental method,
• • Other interconnected pairs of cardiac parameters differ greatly in all five patient groups.

Thus, these open correlations between features of cardiac pathology and correspondingly hidden latent commonality and difference in contexts within the groups selected from the original entirety, even in a relatively uniform system of carefully selected carriers of cardiac pathologies, including subgroups of 10-20 patients, reflect very much complex structure of disease development.

As a result, research into the nature of interrelationships between independent variable parameters in high-volatility groups with significant random deviations from any number of patients makes the task of achieving clinical goals difficult to imagine for the researcher, for whom pathology already looks like a cardiopathology and difficult to achieve without the use of procedures for precise and structural diagnosis of cardiopathology and, apparently, any other pathology.

Example 1. Graph of the relationships between clinical, biochemical and instrumental parameters using the example of the second patient group

As an example of creating a graph of relationships between clinical, biochemical and instrumental parameters, parameters of the second group of 12 patients were selected. According to the database available for this group, the following parameters have been analyzed, see Table 4. Table 4 1 gender 18 heart rate 19 systolic blood pressure 22 HGB 23 RBC 24 WBC 27 BSG 28 glucose 29 uric acid 30 creatinine 31 potassium 32 Creatinine Phosphokinase (CPK) 33 Aspartate transferase (AST) 34 Alanine transferase (ALT) 35 Total bilirubin (TBIL) 36 C-reactive protein 37 total cholesterol 38 Low density lipoprotein (LDL) 39 High density lipoprotein (HDL) 40 Triglycerides (TG) 42 fibrinogen 44 Max. Platelet aggregation in the aggregate with ADP 45 Platelet aggregation in aggregate with ADP 46 typical changes in resting ECG 82 ETT interpretation (negative / positive) 86 Intima Media Thickness (IMD) of the ACC 106 urea 107 sodium 108 calcium 109 HbA1c 110 amylase 111 GGT 112 Alkaline phosphatase 113 pathological changes in the LZ ECG 114 atherosclerotic plaque (AP) in the bifurcation of the left ACC 115 atherosclerotic plaque (AP) in the bifurcation of the right ACC 116 Atherosclerotic changes in the coronary arteries after MSCT coronary angiography with intravenous administration> 50% 117 Atherosclerotic changes in the coronary arteries after MSCT coronary angiography with intravenous KM administration <50% 118 Atherosclerotic changes in the coronary arteries after coronary angiography> 50%, 119 Atherosclerotic changes in the coronary arteries after coronary angiography <50% 120 Multiple lesion of the coronary arteries after coronary angiography

All parameters have been added to the categories according to the reference values (N - standard, UN - under standard, TS - over standard, for parameters without reference values: CN - no standard) or according to the categorical feature of presence (yes - no).

Further, the specific probability of each parameter measured by mathematical methods [6, 7] was determined for each patient. By applying factor analysis [8, 9], the effect of each feature on a particular patient could be determined and the data obtained were systematized for the entire cohort of the study. In order to obtain the values of the parameter 41 belonging to the population, the data of Table 3 are to be considered, in which the data of the factor analysis - numerical values of the factor loads calculated from an aggregated data matrix of 12 patients are grouped.

Out of 12 patients, 8 patients were diagnosed with coronary heart disease (CHD) due to clinical and instrumental procedures. The probabilistic method was used to examine those features and relationships that had a significant contribution to the formation of CHD in this patient cohort. In the subgroup of patients without CHD using standard testing methods, a combination of the characteristics responsible for this condition of the patients was determined simultaneously. When analyzing data in Table 3, it is noticeable that two-thirds of the values of all the parameters in the lines of the Parameters 18 and 22, which are of great importance for the development of cardiopathology in this patient cohort, are. Ie. determine seven parameters out of a total of 41 parameters: 37 (cholesterol), 106 (urea), 30 (creatinine), 34 (alanine transferase), 112 (alkaline phosphatase), 35 (bilirubin), 44 (maximum platelet aggregation), 113 ( pathological changes in the LZ ECG), 116 (atherosclerotic changes in the coronary arteries after MSCT coronary angiography> 50.0%), 118 (atherosclerotic changes in the coronary arteries after coronary angiography> 50.0%) a combination of features that are related and are responsible for the development of CHD in the population of 12 patients.

According to the cluster analysis based on measurements of Euclidean distances between parameter values [10], a graph of the correlations of features characterizing the presence of cardiopathology (ie development of CHD) was compiled in the aggregated matrix of the cohort of 12 patients (see 1 ).

An important research task in this case was to determine relationships between the analyzing clinical, laboratory and instrumental characteristics, because it was probable that the very existence of these systemic correlations characterizes the developmental process of CHD.

The creation of the graph of the relationships between clinical, biochemical and instrumental parameters revealed four coherent macrostructures. At the center of the first macrostructure is the parameter 86 (Intima Media Thickness - ÜN), which has a multitude of relationships with other, mainly instrumental, parameters. Interactions 111 (GGT - ÜN), 40 (triglycerides - ÜN), 40 (triglycerides - N) represent the previous macrostructure with the second macrostructure consisting of four parameters: 38 (LDL - TS), 37 (cholesterol - TS ), 38 (LDL - N), 37 (cholesterol - N). The second macrostructure is connected to the third one. In the third macro structure there are 22 parameters connected, of which 9 have a value above the norm (TS). Two of them are instrumental and reflect the patient's cardiac condition (presence of CHD). These are parameters 116 and 118 that are above standard (MSCT coronary angiography> 50% and coronary angiography> 50%). In other words, the interconnected parameters of the third macrostructure reflect the presence of CHD in patients. The interlinked parameters of this macrostructure show the state of CHD in the form of an internal structure of the relationships between instrumental, clinical and biochemical blood parameters. Of these related parameters, 7 biochemical blood parameters are above the norm (TS): 23 (RBC - TS), 111 (TSG - TS), 32 (CPK - TS), 28 (glucose - TS), 42 (fibrinogen - TS), 110 (amylase - TS), 31 (potassium - TS).

In addition, the values of eleven other parameters contained in the third macrostructure are below the norm (UN), in the normal range (N) and not a norm (CN). 7 of them are clinical and instrumental: 86 (intima-media thickness - UN), 19 (systolic blood pressure - UN), 19 (systolic blood pressure - N), 46 (changes in resting ECG - CN), 82 (ETT interpretation - CN), 117 (MSCT coronary angiography <50%), 119 (coronary angiography <50%). The remaining 4 interrelated parameters are laboratory parameters: 23 (RBC - UN), 112 (alkaline phosphatase - UN), 40 (triglycerides - N), 31 (potassium - UN). The fourth macrostructure, connected in the same way as the third macrostructure, with the second macrostructure (which can generally be called "cholesterol core") consists of 21 interconnected parameters. In addition, only three of them are: 115 (atherosclerotic plaque in the bifurcation of the right ACC - stenosis - TS), 82 (ETT interpretation - N), 119 (coronary angiography <50%) instrumental. Parameter 115 has a maximum number of relationships. The others are the parameters of clinical and biochemical blood analysis and are within the normal range: 44 (maximum platelet aggregation - N), 108 (calcium - N), 30 (creatinine - N), 110 (amylase - N), 32 (CPK - N) , 29 (N -), 39 (HDL - N), 28 (glucose - N), 31 (potassium - N), 27 (BSG - N), 24 (WBC - N), 23 (RBC - N), 35 (bilirubin - N), 22 (HGB - N), 33 (AST - N), 18 (heart rate - N), 112 (alkaline phosphatase - N), 42 (fibrinogen - N). Table 5. Factor loads according to the characteristics to be investigated in 12 patients Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 (+1.0) - (+ 0.72) (+0.71) - (+ 0,40) (+0.39) - (0.0) (-0.01) - (-0.39) (-0.40) - (-0.71 (-0.72) - (-1,0) N Ü N U.N. N Ü N U.N. N Ü N U.N. N Ü N U.N. N Ü N U.N. N Ü N U.N. 1 + 2 + 3 4 + + 5 + + + + 6 + + + 7 + + + 8th + + + + 9 + + + 1 0 + + 1 1 + + + 1 2 + + + 1 3 + + + 1 4 + + + 1 5 + + + 1 6 + + + 1 7 + + + 1 8 + + + 1 9 + + + 2 0 + + + 2 1 + + + + 2 2 + + + 2 3 + + + 2 4 + + + 2 5 + + + 2 6 + + + 2 7 + + + 2 8 + + + 2 9 + + + 3 0 + + + + 3 1 + + + 3 2 + + + 3 3 + + + 3 4 + + 3 5 + + + 3 6 + + + 3 7 + + + 3 8 + + 3 9 + + + 4 0 + + + 4 1 + + +

This example thus shows that patients can be divided into two groups - with or without illness - using standard examination methods. Precisely for patients whose pathology has not been established, it is necessary to make a more accurate diagnosis possible by means of the proposing procedure. By making such a diagnosis, a subgroup in which the latent development of the disease has already begun can be separated from a group of patients without pathology, and the factors primarily responsible for the development of the pathology can be ascertained.

Example 2. Comparison of the patient 73 with the group of 86 patients

• • von +1,00 bis 0,72 als stark wirkend,
• • von 0,71 bis 0,40 - als mäßig wirkend,
• • von 0,39 bis 0,01 - als schwach wirkend definiert.

As an example of the application of the method for the diagnosis of noncommunicable diseases based on statistical methods of data processing in patients with unrecognized pathology, a concrete example is given here. To be considered are results of the processed observations of measurements on 105 parameters reflecting cardiopathology in the group of 86 patients and in patient 73 randomly selected from the group for the purpose of analysis. The results of processing the data matrix of the group and the patient as a part of this group are as follows. Table 6. Factor Weights for 105 Group Parameters with 86 Patients and the Patient 73 Values of the factor weights Group 86 parameters Parameters of the patient 73 F1 (+1,0) - (+0,72) 84, 91, 92, 93, 96 1, 3, 4, 21, 47, 48, 49, 51, 62, 63, 64, 65, 68, 69, 70, 71, 72, 73, 77, 78, 79, 82, 84, 88, 91, 92, 93, 94, 95, 96, 103 F2 (+0.71) - (+0.40) 2, 6, 61, 65, 85, 86, 87, 88, 95, 97, 98, 99 5, 6, 10, 11, 12, 13, 14, 15, 46, 66, 67, 81, 83, 87, 89, 90, 97, 98, 99, 100, 101, 102, 104 F3 (+0.39) - (+0.01) 3, 4, 7, 8, 9, 11, 13, 21, 27, 28, 30, 31, 32, 35, 37, 38, 41, 42, 44, 46, 47, 49, 50, 51, 5253, 56, 57, 58, 60, 64, 67, 68, 70, 73, 77, 79, 80, 82, 89. 90, 94, 100, 101, 102, 103, 104, 105 2, 7, 8, 9, 16, 18, 19, 20, 22, 23, 24, 26, 28, 29, 30, 31, 32, 33, 34, 37, 38, 40, 41, 42, 43, 44, 45, 50, 52, 53, 54, 56, 57, 58, 59, 60, 61, 74, 75, 76, 80, 85, 86, 105 F4 (-0.01) - (-0.39) 1, 5, 10, 12, 14, 15, 16, 17, 18, 19, 20, 22, 24, 25, 26, 29, 33, 34, 36, 39, 40, 43, 45, 48, 54, 55, 59, 62, 63, 66, 69, 71, 72, 74, 75, 76, 78, 81, 83 17, 27, 35, 36, 39, 55 F5 (-0.40) - (-0.71) 0 0 F6 (-0.72) - (-1.0) 0 0 After absolute weighting, the factors are with the value:

• • from +1.00 to 0.72 as strong,
• • from 0.71 to 0.40 - as moderate,
• • from 0.39 to 0.01 - defined as weak.

Bold numbers are important parameters of cardiopathology.

According to the results of the factor analysis, according to the data of the above Table 6 of 105 parameters, a series of strongly and significantly active-acting parameters (see lines 1 and 2 of Table 6) found among others by means of traditional cardiac diagnosis were determined.

In addition, it should be noted that the number of significant cardiac parameters of the patient 73 is twice as high as the number of all significant cardiac parameters of the group. Following the results of the factor analysis, we selected three parameters 79, 87, 91 for processing by the multiple regression to develop a procedure for the accurate diagnosis of a patient's cardiopathology. Table 7. Multi-regression data processing results for the 86 patient and patient group 73. No Dependent variable Number of independent variables that determine the value of dependent variables Absolute total value of the dependent variable Share of the total value of independent variables that acts on the increase of dependent variables Proportion of the total value of independent variables that affects the lowering of dependent variables group Patient 73 group Patient 73 group Patient 73 group Patient 73 1 79 84 4 35.8 7.24 51.2% 51.5% 48.8% 48.5% 2 87 84 4 56.4 6.05 44.1% 52.7% 55.9% 47.3% 3 91 84 8th 47.0 3.12 55.9% 63.1% 44.2% 36.9% Data from the 86 patient group reflect available values for 84 parameters, and the remaining 21 parameters were not included in multiple regression calculations. The number of independent variables of the patient 73 is much lower. The total value of the dependent variable 79, 87, 91 determines the total value of the specified dependent variable by the coefficients of independent variables. The fraction of the total values of the independent variables acting on the increase of the values of that and that dependent variable reflects the measured state of the differential influence of the independent variables on the total value of each of the selected dependent variables. The proportion of the total values of the independent variables acting on the decrement of the and those dependent variables reflects the measured influence on the state of each of the selected dependent variables.

It can be seen from the above Table 7 that the group data confirm the development of cardiopathology in the entirety of patients. The data from the patient 73 demonstrates a more structured and differentiated condition of the cardiopathology in the patient 73. The increase in the value of the dependent parameter 79 determines positive values of the independent parameters 14, 15 and 32. Accordingly, the following independent parameters 18 and 87 act on the decrease of the Value of the dependent parameter.

The increase in the value of the dependent parameter 87, which also has a positive value, depends on two independent parameters 15 and 32. Accordingly, two independent parameters 1 and 18 act on the decrease of its value.

The dependent parameter 91 is maximum and reaches the value of 63.1% mainly because of the independent parameters 1, 14, 15, 32.

Example 3. Comparison of the patient 18 with the subgroup of 26 patients

Let's go to the analysis of data from the third subgroup of 23 patients and to research the data of patient 18 randomly selected from this group for analysis. Table 8. Factor weights for 105 subgroup parameters with 26 patients and patient 18 Values of the factor weights Group 33 parameters Parameters of the patient 18 1 2 3 F1 (+1,0) - (+0,72) 0 1, 3, 5, 7, 8, 9, 11, 21, 47, 49, 51, 53, 62, 64, 65, 68, 69 70, 73, 77, 78, 79, 82, 84, 85, 88 , 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 103 F2 (+0.71) - (+0.40) 14, 16, 17, 22, 23, 34, 36, 54 27, 33, 34, 35, 36, 42, 45, 66, 67, 81, 83, 101, 104, 105. F3 (+0.39) - (+0.01) 10, 11, 12, 13, 15, 18, 19, 20, 2, 4, 6, 10, 12, 13, 14, 15, 18, 19, 20, 22, 23, 24, 25, 26, 28, 29, 31, 39, 41, 43, 48, 50, 52, 54, 55, 56, 57, 59, 60, 61, 63, 71, 72, 74, 75, 76, 80, 86, 87, 89 21, 24, 25, 26, 31, 32, 33, 37, 40, 43, 44, 52, 55, 62, 63, 64, 67, 68, 69, 70, 71, 74, 76, 80, 81, 101, 102 F4 (-0.01) - (-0.39) 1, 3, 4, 5, 7, 8, 9, 28, 29, 30, 35, 38, 39, 41, 42, 45, 46, 47, 48, 50, 51, 56, 58, 59, 60, 65, 66, 72, 73, 75, 77, 78, 79, 82, 85, 89, 90, 94, 97, 100, 103, 105 16, 17, 30, 32, 38, 40, 44, 58 F5 (-0.40) - (-0.71) 2, 6, 27, 49, 53, 57, 61, 83, 84, 86, 87, 88, 95, 96, 98, 0 104 F6 (-0.72) - (-1.0) 91, 92, 93, 99 0

It can be seen from Table 8 above that in this case, as in the previous tables, the factor weights of the strong and moderate clinical, biochemical and instrumental parameters that determine the condition of a patient's cardiopathology outweigh the group data. The development of cardiopathology in the patient 18 is hardly hampered by the parameters whose numbers are given in line F4 and whose number is twice as little as the strong and clear-acting parameter. A specific picture of the condition and structure of cardiopathology in patient 18 can be illustrated by Table 9 below. Table 9. Multi-regression data processing results for the 26 patient and patient subset 18 No Dependent variable Number of independent variables that determine the value of dependent variables Absolute total value of the dependent variable Share of the total value of independent variables that acts on the increase of dependent variables Proportion of the total value of independent variables that affects the lowering of dependent variables group Patient 18 group Patient 18 group Patient 18 group Patient 18 1 79 24 9 21.1 3.2 39.7% 78.3% 60.3% 21.7% 2 87 24 8th 26.8 4.7 57.3% 51.6% 42.7% 48.4% 3 91 24 8th 13.0 2.7 53.6% 79.0% 46.4% 21.0%

According to the group data, the values of the cardiologically dependent variable 79 are determined to be equations of multiple regression increasing by 39.7% in terms of coefficients, but the same variable is reduced by 60.3% from the values of the coefficients of multiple regression of independent variables. The dependent variable 87 is increased by already 57.3% by the coefficient values of the independent variable equation, while it is thereby lowered by 42.7%. For the dependent variable 91, corresponding values increase by 53.6% and decrease by 46.4%.

The data of the patient 18 are as follows: the dependent variable 91 has in Table 9 the maximum value of the independent parameters increasing it - 79.0%. Immediately thereafter, the maximum value of the dependent variable 79 follows at 78.3%. For the dependent variable, this value also has a dominant value, which increases its growth to 51.6%. As can be seen, the cardiopathology in patients 18 is more pronounced compared to the data of the group. This condition is determined by positive values of independent cardiac parameters 1, 13, 14, 15, 17, 38, 60, 75, 87 for three dependent variables, which in this case reflect a possible development of cardiopathology in the patient 18.

SUMMARY

These above examples have illustrated that the uniqueness of the method is that it allows a subset of patients who are in an intermediate state between no disease and the onset of disease to be selected from a group of healthy patients selected by standard screening methods. This is extremely important - as this subgroup requires special attention and active treatment. This does not happen in the traditional analysis of the situation, because patients are not adequately treated without signs of the disease. The reason hidden in the genesis of such development of the disease in a particular patient, on which are also formed their own subgroups, is that individual features - triggers, which may overlap with the group characteristics in repeating their occurrence in different patients, in a particular one Combining the disease.

This is confirmed by the information obtained in our examples. Patients 73 and 18, selected from various subgroups formed using cluster analysis, currently have no cardiovascular disease. This was not confirmed by any existing atherosclerotic lesions of the carotid and coronary arteries in duplex examination and coronary angiography.

• • beim Patienten 73 sind das Parameter 1 (Geschlecht), 14 (klinische Manifestationen der Krankheit - in diesem Zusammenhang - Beschwerden des Patienten), 15 (Wuchs), 18 (Herzfrequenz bei Aufnahme), 32 (CPK).
• • beim Patienten 18 sind das Parameter 1 (Geschlecht), 13 (regelmäßiger Stress), 14 (klinische Manifestationen der Krankheit - in diesem Zusammenhang - Beschwerden des Patienten), 15 (Wuchs), 17 (Body-Mass-Index), 38 (LDL), 60 (linksventrikuläre Hinterwanddicke nach Echokardiogramm), 75 (maximale Herzfrequenz beim ETT).

In the additional analysis and compilation of graphs of the interrelationships of features that influence the further development of the disease in a particular person by interrelations with the main features (79.87.91), the following combinations were obtained:

• • in the patient 73, the parameters are 1 (sex), 14 (clinical manifestations of the disease - in this context - complaints of the patient), 15 (growth), 18 (heart rate at admission), 32 (CPK).
• • in patient 18, the parameters are 1 (sex), 13 (regular stress), 14 (clinical manifestations of the disease - in this context - patient complaints), 15 (growth), 17 (body mass index), 38 ( LDL), 60 (left ventricular posterior wall thickness after echocardiogram), 75 (maximum heart rate at ETT).

When comparing the features, similar parameters are seen in two patients and, at the same time, a number of features in the patient 18 make it clear why he is in another group - a latent transition group in which the development of the disease has already begun , This is characterized by the known risk factors - stress (13), obesity (17), lipid factors (38), myocardial hypertrophy (60), no physical activity (75). In this combination, they have become triggers for the onset of the disease. This requires immediate treatment with non-drug and medical methods. In this example, triggers became known factors. However, the peculiarity and interest in the proposing procedure is that in a patient, the characteristics that require treatment but do not fit into the understanding of disease development through classical procedures and traditional data analysis can be discarded. By way of example, such parameters as established in this invention, such as the blood group in the development of CHD, GGT in the development of cancer, etc., may serve.

• • die Messung der Einwirkungen von Merkmalen auf den Zustand des Patienten ermöglicht,
• • dem Forscher ein wirksames Instrument zur Erreichung der von ihm gestellten Ziele gibt,
• • sowohl die Gesamtstruktur der allgemeinen Pathologie als auch ihre individuellen Merkmale für einen bestimmten Patienten bestimmen lässt.

Aufgrund der obigen Merkmale wird somit das erzielte technische Ergebnis zugesichert, und zwar die Verbesserung der Diagnosegenauigkeit nichtübertragbarer Krankheiten bei einem bestimmten Patienten durch Aufdeckung zusätzlicher Faktoren, die die Entwicklung dieser Krankheiten bei einem Patienten beeinflussen.Accordingly, the presented calculations using the example of cardiovascular diseases have shown that the procedure for the diagnosis of noncommunicable diseases is based on statistical methods of data processing

• • allows the measurement of the effects of features on the condition of the patient,
• • gives the researcher an effective tool to achieve the goals he has set,
• • determine the overall structure of the general pathology as well as its individual characteristics for a particular patient.

Due to the above features, the achieved technical result is thus assured, namely the improvement of the diagnostic accuracy of non-communicable diseases in a particular patient by detecting additional factors that influence the development of these diseases in a patient.

• • klinische Praxis im Gesundheitswesen wesentlich verbessern lässt,
• • auch für die Lösung anderer medizinischer Probleme, insbesondere für die

Behandlung onkologischer Pathologien erfolgreich angewendet werden kann. Darüber hinaus öffnet sich noch ein wichtiger Bereich im Gesundheitswesen, der durch die Anwendung des vorschlagenden Verfahrens zugesichert wird. Ein richtig festgestellter Parameter-Trigger bei einem Patienten und die entsprechende komplexe Anwendung medizinischer Verfahren müssen zur Besserung des Patienten führen. Danach sind neue Messungen des neu entstandenen Systems von miteinander zusammenhängenden Parametern durchzuführen. Dann verschwindet das „alte“ Pathologiezentrum im neuen System und entsteht ein „neues“ Zentrum, das durch nachfolgende medizinische Maßnahmen „zunichte gemacht“ wird. Und so geht es schrittweise, bis zur vollständigen Beseitigung der Pathologie. Mittels des vorschlagenden Verfahrens kann das Beziehungssystem zwischen den Parametern eines Patienten berechnet werden. Darin ist der zentrale Parameter festzustellen, der den Status jeder Pathologie bestimmt. Moderne Standardverfahren sichern die Möglichkeit der Beseitigung oder Elimination einer verbunden abhängigen Variable zu, dadurch wird die Pathologie nicht völlig zerstört, sondern nur aufgrund der Interferenzwirkung von Krankheiten, wenn eine Pathologie eine andere überlappt, modifiziert. Aber jetzt ist es möglich, durch die schrittweise Beseitigung einer Anzahl pathologischer Kerne in Form von Graphenscheiteln (abhängigen Parametern mit vielen Zusammenhängen) alle Körperparameter eines Patienten auszugleichen, d. h. ihn in einen natürlichen, gesunden Zustand zu bringen.Another useful technical result of the claimed invention is that this method of diagnosing noncommunicable diseases is based on statistical methods of data processing

• • significantly improve clinical practice in healthcare,
• • also for the solution of other medical problems, especially for the

Treatment of oncological pathologies can be successfully applied. In addition, there is an important area of public health, which is guaranteed by the application of the proposed procedure. A properly established parameter trigger in a patient and the corresponding complex application of medical procedures must lead to the improvement of the patient. Thereafter, new measurements of the newly created system of interrelated parameters are to be performed. Then the "old" pathology center disappears in the new system and creates a "new" center, which is "nullified" by subsequent medical measures. And so it goes gradually, until complete removal of the pathology. By means of the proposed method, the relationship system between the parameters of a patient can be calculated. This is the central parameter to determine the status of each pathology. Modern standard procedures ensure the possibility of elimination or elimination of a linked dependent variable, thereby not completely destroying the pathology but only modifying it because of the interference effect of diseases when one pathology overlaps another. But now it is possible, by the gradual elimination of a number of pathological nuclei in the form of graphene vertices (dependent parameters with many contexts), to balance all body parameters of a patient, ie to bring him into a natural, healthy state.

Claims (6)

The method of diagnosing noncommunicable diseases based on statistical methods of data processing, wherein the parameters characterizing the condition of a patient are measured and detected patient parameters are manipulated by statistical methods and thereby the most significant diagnostic parameters whose deviation from the norm for the presence of a disease speaks is characterized in that • the parameters characterizing the condition of a patient, which include history, clinical, laboratory and instrument data, are measured; • for the parameters with reference intervals, the arithmetic mean is calculated based on the parameter value of a specific patient and the values of the reference intervals. Other parameters are assigned the value by categorical feature of existence and are referred to as 1 or 0. • a personal matrix is created with clinical and biochemical data of the patient, which is formed by binary coefficients. The binary coefficients correspond to the comparison of each parameter with a personal reference value and each parameter within an interval of deviations of the measured value from the personal reference value of the specified parameter. The parameters characterizing the condition of the patient are sequentially stored in the columns of the matrix, and the norms and different deviation intervals of the measured value from the personal reference value of the specified parameter are arranged in the rows of the matrix. The binary coefficient is assigned 1 if the parameter value is within the specified interval, and 0 if not. The patient's clinical and biochemical data obtained is processed by statistical methods, thereby determining the most significant parameters influencing the condition of the patient, by which the presence of the disease in the patient may be recognized. The procedure for the diagnosis of noncommunicable diseases Claim 1 is characterized in that the most significant diagnostic parameters are selected from a group of parameters showing relationships within the group. The procedure for the diagnosis of noncommunicable diseases Claim 1 is characterized in that the personal matrix of the clinical and biochemical condition of the patient is processed by means of statistical methods using cluster analysis in order to determine the structural relationships from all possible parameter relationships. The procedure for the diagnosis of noncommunicable diseases Claim 1 characterized in that the obtained personal patient matrix is manipulated using factor analysis to determine factor weights of each parameter, its sign and direction of action (positive or negative) under the effect of each of the overall patient parameters. The procedure for the diagnosis of noncommunicable diseases Claim 1 is characterized in that the obtained personal patient matrix of the clinical and biochemical condition of the patient using multiple regression to determine the influence of independent patient parameters on dependent patient parameters. The procedure for the diagnosis of noncommunicable diseases Claim 1 is characterized in that for the parameters with reference values, a reference value change of the specified parameter is made in the direction of the values of the patient parameter measured at different times, thus the personal reference value of the specified parameter is determined by the patient for a specific period of time.

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