=Paper=
{{Paper
|id=Vol-2254/10000214
|storemode=property
|title=Development
of an intelligent system of complex diagnostics of human bioelement
status
|pdfUrl=https://ceur-ws.org/Vol-2254/10000214.pdf
|volume=Vol-2254
|authors=Sergey Miroshnikov,Irina Bolodurina,Olga
Arapova,Denis Parfenov
}}
==Development
of an intelligent system of complex diagnostics of human bioelement
status==
https://ceur-ws.org/Vol-2254/10000214.pdf
Development of an intelligent system of complex
diagnostics of human bioelement status
Sergey A. Miroshnikov Irina P. Bolodurina
Federal Scientific Center of Biological Systems Department of Applied Mathematics
Orenburg 460018, Russia Orenburg State University
sergey ru01@mail.ru Orenburg 460018, Russia
ipbolodurina@yandex.ru
Olga Arapova Denis I. Parfenov
Department of Applied Mathematics Faculty of Distance Learning Technologies
Orenburg State University Orenburg State University
Orenburg 460018, Russia Orenburg 460018, Russia
sando@mail.ru parfenovdi@mail.ru
Abstract
The creation of an information intelligent preliminary diagnosis system
is an important means of monitoring people’s health. Within the frame-
work of the developed intellectual system, preliminary prediction of the
level of trace elements content is considered without the sampling of
the analysis. As a result of processing of experimental medical data of
analyzes of microelement composition, a system of product rules was
developed, which is the basis for making diagnostic decisions on the
level of trace elements in the human body on the basis of an intelligent
system of preliminary diagnosis. The paper considers the problem of
the influence of environmental factors on the health of people. The
results of the application of the integral index model, generalizing the
characteristics of twelve chemical elements, to the assessment of the
health status of people employed in industrial enterprises are presented.
1 Introduction
One of the most important state tasks laid down in the state program for the development of the healthcare of
the Russian Federation is to ensure an increase in the life expectancy of the population of the country to 74.3
years by 2020. The key factor affecting the life expectancy of the population is the health status of members of
civil society. The lifespan of the population directly depends on resources that influence the development of any
state. According to the World Health Organization, the largest contribution to the health of the population is
made by a group of factors united by the concept of external environment, which includes numerous elements
that pollute air, water, food, soil, as well as factors such as noise, vibration, radiation and others. The main
source of their origin is the diversified industry. At the same time, studies aimed at studying the influence of
Copyright c by the paper’s authors. Copying permitted for private and academic purposes.
In: Marco Schaerf, Massimo Mecella, Drozdova Viktoria Igorevna, Kalmykov Igor Anatolievich (eds.): Proceedings of REMS 2018
– Russian Federation & Europe Multidisciplinary Symposium on Computer Science and ICT, Stavropol – Dombay, Russia, 15–20
October 2018, published at http://ceur-ws.org
environmental factors on the health status of people working in hazardous industries are becoming particularly
relevant [Roc08].
Anthropogenic pollution of the environment is mainly associated with microelements from the group of heavy
metals. At present, technogenic microelements are becoming increasingly important. In the immediate vicinity
of many industrial enterprises, zones with a high content of various toxic trace elements are formed, including
radioisotopes, which pose a threat to health and even human life[Mir13]. The negative impact of these factors on
public health is a serious concern for public authorities responsible for both the state of health of the population
and the protection of the environment in various states.
In order to comply with the requirements of the state policy in the field of environmental protection, it
is necessary to carry out constant monitoring of the health of the population. One of the most important and
mandatory conditions for maintaining health is the stability of the chemical composition of the body. Accordingly,
deviations in the content of useful chemical elements or the accumulation of toxic elements in the human body
can be not only a criterion for environmental ill-being, but also serve as markers at the level of donor diagnosis
of abnormalities in health status [Ska15].
In this regard, it is necessary to develop a comprehensive indicator that allows integrating the numerical
characteristics of a person’s bio element status into an overall assessment of his health status. An assessment of
the microelement status of a person is necessary for an adequate assessment of the situation and the resolution
of emerging problems [Kob13].
The definition of human elemental status is possible through the analysis of biological substrates such as blood,
urine, hair, and others. Each of these substrates has certain informativeness. Note that the most effective and
informative method for testing and diagnosing a person’s health status is the analysis of trace elements contained
in human hair. According to modern ideas, hair reflects the elemental status over a long period of time. In hair,
it is possible to trace the change in the content of an essential or toxic element that occurs when long-term
effects of certain factors specific to specific regions, including the environment. It is established that the hair of
people living in different countries differs in their chemical composition. This is due to the different contents of
chemical elements in drinking water, food, climate, biogeochemical, social, occupational and physiological factors
also exert a great influence[Ska15].
However, it should be noted that conducting a full analysis of all trace elements takes a long time and requires
significant financial costs. To obtain reliable information, it is necessary to automate the processes of analysis
and processing of collected data. In addition, the need to develop an intelligent system for the diagnosis of
bioelement status is due to the following factors[Ska15]:
- in a real situation, the doctor has to process a significant amount of clinical, laboratory and instrumental data
about the individual characteristics of the patient. Integration of a large number of data, their interpretation,
classification of objects is a complex problem;
- the decision has to be taken when an incomplete, uncertain, subjective and contradictory information comes
to a–priori;
- the need to minimize financial and time costs for the diagnosis and processing of analysis data.
In the framework of this study, the goal is to form the knowledge base of the intellectual system for preliminary
diagnosis of the level of trace elements in the human body using methods of data mining.
2 Related work
It is known that unfavorable factors of anthropogenic nature predominantly affect the elemental status of the
population engaged in harmful industries, primarily mining, metallurgy, machine building[Ska00, Ska02, Say90,
Aga00, May00, Boe02].
The level of trace elements in the human body is affected by external and internal factors. An important
external factor is a prevalence in the earth’s crust since the main source of chemical elements for humans are food
and water, with which chemical elements enter the gastrointestinal tract. In works [Aft12, Ska12-2] it is noted
that other ways of penetration of microelements - with inhaled air, through the skin and others are possible.
Other external factors are also important: the aggregate state of the natural compounds of microelements, the
solubility in water, the form of entry into the body, for example, in plants the elements are in biologically active
concentrations, so it is better absorbed by the human body.
Internal factors that affect the content and manifestation of the physiological role of chemical elements include
their distribution in various organs and tissues, the form of the presence of chemical elements, which affects the
stability constant of the coordination compound, and also the position in the periodic system that determines
the manifestation of all their biological responses [Ska11, Not10].
In addition, the content of trace elements depends on the person’s age, the state of functional activity, biological
and diurnal rhythms, the presence of a pathological process, including stress, anesthesia and so on [Lit97, Chi13].
According to the method of Dr. Skalny, deviations in health status are determined and predispositions to the
development of diseases due to genetic inheritance, environmental influences, lifestyle, nutrition and other factors
are identified study of trace elements contained in the human body. Their deficiency or excess is an accurate
indicator of the diseases of the body or directly the cause of the disease [Ska03, Ska00]. The paper considers
the influence of the following main factors: the area of residence, the age group, the type of food and the sex
of the person. In Russia, 13 regions are marked as regions with a critical ecological situation. The decline in
production is not accompanied by a proportional decrease in the technogenic burden on the environment. On
the contrary, the deterioration of the conditions for the existence of certain industries leads to the fact that, first
of all, environmental protection measures and cleaning systems are neglected[Nov02].
In recent works [Don94, Boe04], a clear relationship between the state of human health and environmental
factors is reflected. Currently, global pollution of the environment is noted for technogenic products, which,
having increased mutagenic activity, carry the danger of affecting not only the general state of health but also
the genetic apparatus of people[Nov02].
The works of Izrael Yu.A., Donchenko VK, Muravya LA, Hotuntseva Yu.L. are devoted to the issues of
ecological and ecological-hygienic monitoring [Nov02, Isr77]. At the present time, a lot of data have been
accumulated confirming the dependence of the elemental composition of living organisms (conditionally essential
and toxic (Al, As, B, Be, Cd, Hg, Li, Ni, Pb, Sn, V) and vital (I, Ca, Co , Cr, Cu, Fe, K, Mg, Mn, Na, P, Se, Si,
Zn)), including humans, from the content of chemical elements in the habitat, i.e. the composition of the internal
environment of the body is influenced by the external. One of the markers, which does not react to short-term
changes in nutrition and is able to reflect the picture of the provision of chemical elements of the body for
several months, is the hair. According to most authors, hair analysis can be used not only for individual health
assessment but also for assessing the general health status of a group of people [Ska00]. The carried out analysis
of researches and existing software products of monitoring of the environment allows to draw conclusions that
for the solution of environmental problems formed as a result of environmental pollution (industrial character),
it is necessary to develop a modern information system for diagnosing the bioelement status of a person, built
using intelligent methods of data analysis (Data Mining). Such a system will allow solving the problems of
timely determination of the impact of harmful substances on human health on the basis of an analysis of the
accumulated information on the state of the environment and the medical status of the region in which industrial
enterprises are located.
3 Materials and methods
To identify the exchange of microelements in the human body and the toxic effects of certain heavy metals, as
previously identified, of particular interest is the study of hair. The content of microelements in the hair reflects
the microelement status of the organism as a whole. Consequently, hair samples are an integral indicator of
mineral metabolism[Ska03, Ska12].
However, within the framework of this study, it has been established that when analyzing the bioelemental
data of human hair, certain difficulties arise. First of all, this is due to the fact that it is necessary to assess
the state by a large number of indicators. In this case, the deviation of indicators from ”normal” values is
accompanied by random fluctuations caused by various factors. The large dimensionality of the state vector and
the random nature of the changes in its components make it difficult to assess the state of health. This, in turn,
makes it difficult to assess the anthropogenic impact of the state of the environment on a person.
To solve the described problems, we introduce a generalized indicator that is a scalar function of the state
vector. The components of the vector within the framework of this study will be considered clinico-laboratory
indicators of the bioelement composition of the hair.
In this study, the determination of trace element status is carried out on the basis of analysis of the content
of trace elements in human hair in percentiles by means of atomic emission spectrometry. Then we will assume
that the content of microelements in the body is characterized by a vector X = x1 , x2 , . . . , xn , the components
of which are the percentile values of toxins and vital elements in the hair within the framework of the three
groups. For classification into groups, we will use the following percentile scale, currently used by physicians: -
the first group - the content of the microelement is up to 25 percentiles, then there is a deficiency of this trace
element; - the second group - the content of the microelement is in the range of 25 to 75 percentiles, then the
level of content is normal; - the third group - the content of the microelement is more than 75 percentiles, then
the excess content of the microelement is observed.
Then, within the framework of the study, the problem is to construct a scalar function of the state vector
φ = (X) that estimates the value of s (the ideal value corresponding to an absolutely balanced exchange of
substances) with a minimum error. The function φ = (X) is called a generalized exponent. The function
φ = (X) is chosen from the condition of the minimum mean square error of the prediction of the value s:
E[ϕ(X) − s]2 → min (1)
or in the form of a complete mathematical expectation:
X
E[ϕ(X) − s]2 = ps E[(ϕ(X) − s)2 |s], (2)
s
where ps probability of state with number s. P
n
For φ = (X) we choose a linear form: ϕ(X) = i=1 ai xi , ai xi = 1.
P
Coefficients of the generalized
P index i(i = 1, ..., n) a re determined from the condition for the minimum of the
expression (2) on the set a : ai = 1. The method for finding the coefficients of the generalized exponent is
described in[Aft12]. Thus, the scalar function of the state vector is defined.
4 Practical implementation
The present study was conducted on the example of the database ”Results of medical analyzes of microelement
composition in the city-megalopolis N”. Hair samples were subjected to sample preparation in accordance with
the requirements of Guidelines 4.1.1482-03 and 4.1.1483-03 ”Determination of chemical elements in biological
media and preparations by inductively coupled plasma atomic emission spectrometry and inductively coupled
plasma mass spectrometry”. The elemental composition of the hair was defined by atomic emission and mass
spectrometry (AES and MS) at the Test Laboratory of the ANO BCenter for Biotic Medicine, Moscow (Regis-
tration Certificate of ISO 9001: 2000, Number 4017 5.04.06). The biosubstrates were ashed using the MD-2000
microwave decomposition system (USA). The content of elements in the resulting ash was estimated using the
Elan 9000 mass spectrometer (Perkin Elmer, USA) and an Optima 2000 V atomic emission spectrometer (Perkin
Elmer, USA). The sample size of real medical data of trace element analyzes was 68,000 records, the volume for
each microelement is from 3,064 to 3,452 records. The influence of four factors on the level of 23 microelements on
the human body was studied: arsenic, phosphorus, aluminum, beryllium, calcium, cadmium, cobalt, chromium,
copper, iron, potassium, lithium, magnesium, manganese, sodium, nickel, lead, selenium, silicon, tin, titanium,
vanadium, zinc. The following Table 1 gives a summary sample size data for each trace element, broken down
by possible values of their level content.
The micronutrients presented have different effects on the human body and in a certain concentration are
necessary and safe. The following grouping of microelements is distinguished:
- Minor trace elements: selenium Se, cobalt Co, zinc Zn, manganese Mn, copper Cu.
- Essential trace elements: iron Fe, iodine I, copper Cu, zinc Zn, cobalt Co, chromium Cr, molybdenum Mo,
selenium Se, manganese Mg
- conditionally essential trace minerals: arsenic As, boron B, bromine Br, fluorine F, lithium Li, nickel Ni,
silicon Si, vanadium V, cadmium Cd, lead Pb.
- vital elements: structural (macro) elements - elements - H, O, N, C; Ca, Cl, F, K, Mg, Na, P, S and 8 trace
elements - Cr, Cu, Fe, I, Mn, Mo, Se, Zn.
The full content of essential elements and minimal, not threatening to disrupt the adaptive mechanisms of
the body, the presence of toxic and conditionally toxic elements, is one of the most important components of the
normal functioning of the human body.
To assess the microelement status, it is necessary to consider the entire complex of trace elements, each of
which performs a certain role in ensuring the proper functioning of the organism. The lack of at least one
component can lead to various diseases.
Therefore, the creation of an intelligent intelligence system for preliminary diagnosis is an important means of
monitoring people’s health. Within the framework of the developed intellectual system, a preliminary prediction
of the level of the trace element content is considered without sampling the analysis.
Table 1: The data of the volume of samples of trace elements by content levels
Level of microelement content
Element Scope sampling norm increased lowered
num of rec. % num of rec. % num of rec. %
As 3451 3340 96,78 111 3,22
P 3451 2073 60,07 509 14,75 869 25,18
Al 3451 3032 87,86 242 7,01 177 5,13
Be 3451 3451 100,00
Ca 3451 2104 60,97 480 13,91 867 25,12
Cd 3443 3200 92,94 243 7,06
Co 3451 1652 47,87 13 0,38 1786 51,75
Cr 3452 1908 55,27 503 14,57 1041 30,16
Cu 3452 2396 69,41 451 13,06 605 17,53
Fe 3450 1852 53,68 338 9,80 1260 36,52
K 3450 1721 49,88 666 19,30 1063 30,81
Li 3451 3398 98,46 19 0,55 34 0,99
Mg 3451 1620 46,94 406 11,76 1425 41,29
Mn 3452 1666 48,26 407 11,79 1379 39,95
Na 3450 2038 59,07 703 20,38 709 20,55
Ni 3451 3376 97,83 75 2,17
Pb 3451 3156 91,45 295 8,55
Se 3452 1994 57,76 217 6,29 1241 35,95
Si 3451 2012 58,30 1011 29,30 428 12,40
Sn 3083 2959 95,98 124 4,02
Ti 3248 3128 96,31 120 3,69
V 3064 2964 96,74 100 3,26
Zn 3452 1917 55,53 268 7,76 1267 36,70
When creating model support for the diagnostic system, methods of data mining are used: the algorithm for
constructing the decision tree of ID3 and its improved version of C4.5 and the method of direct logical inference
based on qualitative medical data.
One of the main problems of decision-making in weakly structured areas, such as medicine, ecobiomedicine,
immunology, is the problem of processing and analyzing large amounts of information. In this regard, there is a
need to create intelligent systems. The software implementation of the intellectual system is carried out using
the means ”1C: Enterprise 8.2”. The scheme of the data flows of the intelligent prediagnostic system is shown
in Fig. 1.
Figure 1: Scheme of data flows of an intelligent system for the diagnosis of human bioelement status.
We define the input parameters in more detail in the systems. The patient’s personal data is the data of
the patient’s medical record. The area of residence is a micro-district in the city, where a person lives. Within
the framework of this work, the town of the city N and the special significance of the area of residence are
considered, as there are factories on the territory of the city[Not06]. In terms of its industrial potential, the
metropolitan city of N is considered the largest center in its region. About 20% of engineering equipment for this
huge region is produced at the enterprises of the city of the city N and the region, among them - metalworking
and woodworking machines. Along with heavy industry, electric power and non-ferrous metallurgy are rapidly
developing. The unique enterprises of the city of the city N and the region produce tin and gold, chemical
concentrates, rare metals and nuclear fuel. These types of products are in high demand not only at home and
abroad. The following plants are located on the territory of the city: aircraft, metal structures, plastics, building
materials, instrumental, cable, brick, foundry instrumentation, chemical, electromechanical, jewelry and others.
The next input parameter is the age group that determines the age of the person. It is advisable to consider
it in the age range since the content of trace elements differs in children and in older people due to age-related
changes in the human body. Age is presented in groups: up to 3 years, from 3 to 16 years, from 17 to 49 years
and over 50 years.
The type of food has a significant effect on the content of microelements. Distinguish the following types of
nutrition: breast, breast and lure, mixed, vegetarian, milk-vegetable, meat, diet, hypoallergenic diet. Due to
physiological differences, it is necessary to consider the gender of the patient, who takes two values - male or
female.
The intelligent preliminary diagnosis system implements the following functions: input of personal data about
the diagnosed patient, which includes the patient’s full name and other personal data; extraction of signs (factors)
with the purpose of revealing the level of the content of microelements and forming conclusions on diagnostic
solutions.
The intellectual system contains: a database and knowledge in which data and knowledge about diagnosed
such as last name, first name, patronymic are stored; year of birth; floor; place of residence, type of food;
mathematical apparatus of the mechanism of logical inference; a system of production rules; results of express
diagnostics of the subjects.
After obtaining the value of informative parameters (factors) for the object to be diagnosed, they are stored
in the database and used to formulate conclusions on the diagnosis of the content of trace elements in the human
body. The conclusion about the level of the content of trace elements is formed on the basis of the production
systems of knowledge, presented in the form of multiple rules. As a result of the operation of the inference engine
block, there is a definition of particular dependencies between objects or events.
The dependencies found are represented in the form of rules and can be used both for better understanding
of the nature of the analyzed data and for predicting the occurrence of events.
As the most adequate model for constructing production rules, according to the results of ROC analysis, a
decision tree was chosen based on the algorithm C4.5. Evaluation of the effectiveness of the obtained decision
rules on the examination sample showed fairly good results - with a reliability of 0.8 the objects of the class
are ”correctly increased”, with a probability of 0.86 - objects of the ”norm” class and with a probability of 0.75
objects of the class ”lowered”.
The following Table 2 presented the basis of the production model is laid down the rules obtained by the
algorithm C4.5 for each of the microelements.
For each patient, a new fact is processed according to the rules system with the output of the result on the level
of trace elements content. At the same time, for each trace element, the service information-the rule number-is
indicated.
Based on the results of data analysis, a list of microelements is formed, the level of content of which differs
from the value of ”norm”. For this element, recommendations are made for the doctor to refer the patient to an
additional examination.
Obtained predicted diagnostic data for all trace elements for each specific patient, the doctor uses for further
planning of the survey and the appointment of treatment.
5 Conclusion
Thus, the research suggests a technique implemented in the form of an intellectual support system for making
diagnostic decisions on the level of trace elements in the human body on the basis of data analysis using Data
Mining methods.
The suggested methodology can form the basis of an environmental information system for decision support,
in the process of which an integral indicator of public health is formed, reflecting an assessment of the health of
Table 2: The data of the volume of samples of trace elements by content levels
Element Root Nodes Rules Cutoff threshold Specificity Sensitivity
P Type of food Area-Paul 32 0,41 54,96 57,33
Al Area Gender-Group Group-Gender 24 0,1163 100 73,68
Be Group Area Area-Gender 43 0,0315 62,5 70,73
Ca Type of food Area-Group-Gender 68 0,0376 67,69 70,54
Cd Type of food Area-Group-Gender 35 0,0456 67,61 60,34
Co Type of food Area-Group-Gender 37 0,0412 77,69 75,48
Cr Type of food Group-Area-Gender 70 0,3743 55,42 63,7
Cu Type of food Area-Group-Gender 35 0,0236 52,62 54,48
Fe Type of food Area-Group-Gender 80 0,0446 75,64 56,48
K Type of food Area-Group-Gender 75 0,0487 67,89 72,98
Li Type of food Area-Group-Gender 69 0,0235 76,53 888,93
Mg Type of food Area-Group-Gender 57 0,0657 78,29 70,48
Mn Type of food Area-Group-Gender 121 0,0567 77,49 88,32
Na Type of food Area-Group-Gender 103 0,0456 89,67 98,45
Ni Area Type of food-Group-Gender 30 0,1142 100 53,62
Pb Area Type of food-Group-Gender 24 0,1123 100 83,63
Se Group Gender-Area 47 0,1395 83,33 72,97
Si Group Gender-Area 34 0,1395 83,33 71,97
Sn Area Type of food-Group-Gender 24 0,1132 100 63,68
Ti Group Gender-Area 16 0,1395 83,33 62,67
V Group Gender 5 0,0115 85,88 100
Zn Area Type of food-Group-Gender 24 0,1163 100 73,68
people working in enterprises with a high anthropogenic load. The application of this environmental information
system will allow for constant monitoring of the medical and environmental situation in industrial complexes,
and in the development of measures adequate to the current state of the environment, to prevent situations that
pose a threat to human health, and thus the environment.
Within the framework of the constructed intellectual system, production rule systems based on decision trees
have been formed. Developed by an intelligent system for predicting the level of trace elements in the human
body without taking samples, doctors will be able to determine the level of certain trace elements at the time
of treatment. This will make it possible to choose the optimal scheme for sampling and analysis of the analyzes
for trace elements, based on the individual medical profile of the patient.
6 Acknowledgements
The research was conducted with the support of the Russian Science Foundation (project no. 14-16-00060).
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