admin

Issue 3 (193), article 2

DOI:https://doi.org/10.15407/kvt192.03.027

Kibern. vyčisl. teh., 2018, Issue 3 (193), pp.

Antomonov M.Y., DSc (Biology), Professor,
Chief Researcher of the Laboratory of Epidemiological Research and Medical Informatics
e-mail: antomonov@gmail.com
State Institution “O.M. Marzіeiev Institute for Public Health of the National Academy of Medical Sciences of Ukraine”, 50,  Popudrenko str.  Кyiv, 02660

INFORMATION TECHNOLOGY FOR CONSTRUCTING THE COMPOSITE INDICES FOR DATA OF DIFFERENT TYPES USED IN MEDICAL AND ENVIRONMENTAL STUDIES

Introduction. Information technologies used in medical and environmental researches often deal wiht huge amounts of information processing. These technologies allow us to identify and investigate previously hidden dependencies and interactions in complex environmental, medical and biological systems, and on the other hand, it is accompanied by the analysis of large data sets, some of which (sometimes most of them) have an uninformative (noisy) character. One of the ways of solving this problem are the methods of constructing composite indices (CI), i.e. complex indicators, which allow to perform an integral assessment of the state and functioning of ecological, medical and biological systems.

The purpose of the paper is to develop a generalized information technology for constructing composite indices for different types of data used in medical and environmental studies.

Results. Medical and ecological researches include two main components: analysis of the states of both human health and of the environment; in solving such problems it is necessary to evaluate and analyze the state of the bioobject according to the data of different types: quantitative, rank, binary and qualitative variables. The developed general information technology is oriented on supporting the solution of a wide range of medical and hygienic tasks and integrates various approaches to processing and analysing of data of different types. Proposed technology consists of four stages: the formation and initial analysis of an initial indicators set, the calculation and normalization for obtainig unnamed equivalents, the actual design of the composite indices, and their verification. The implementation of this technology makes it possible to compare data of different dimensions, determine the significance of specific characteristics in a general research totality, to evaluate the integral state and to classify the research objects.

Conclusion. The proposed information technology for the construction of composite indices based on data of different types: quantitative, rank, binary and qualitative variables, is an effective tool for determining and comparing the state of bioobjects of different nature, and its use makes it possible to avoid mistakes in the incorrect application of mathematical methods for processing medical and ecological information.

Keywords: information technology, composite indicators, processing medical and ecological quantitative, rank, binary and qualitative variables.

Download full text!

REFERENCES
1.      Suter, E, et al. Indicators and Measurement Tools for Health Systems Integration: A Knowledge Synthesis. International Journal of Integrated Care, 2017; 17 (6): 4, 1–17. DOI: https://doi.org/10.5334/ijic.3931
2.    T. Hastie, R. Tibshirani, J. Friedman. The elements of statistical learning / data mining, inference, and Prediction. Second edition, 12th printing 2017, 745 p.
3.    Antomonov M.Yu., Voloshchuk E.V. Constructing integral indicators of quantitative characteristics using one-dimensional and multidimensional statistical methods. Kibernetika i vyčislitel’naâ tehnika. 2012. Iss. 167. P. 61–68 (in Russian).
4.    Mikheienko О.І. Integrated method for assessing the health of the human body. Pedahohika, psykholohiya ta medyko-biolohichni problemy fizychnoho vykhovannya i sportu. 2011. Iss. 6. P. 93–101 (in Ukrainian).
5.    Apanasenko G.L. Diagnosis of individual health. Gigiyena i sanitariya. 2004. Iss. 1. P. 55–58 (in Russian).
6.    Merkov A.M., Polyakov L.E. Sanitary statistics (manual for doctors). Moscow: Meditsina, 1974. 384 p. (in Russian).
7.    Bulich E.G., Muravov I.V. Human health: The biological basis of vital activity and motor activity in its stimulation. Kiev: Olimpiyskaya literatura, 2003. 424 p. (in Russian).
8.    Apanasenko G.L. The book is about health. Kiev: Medkniga, 2007. 132 p. (in Russian).
9.    Bezruk V.V. Anthropometry. Assessment of physical development of children. Methods of evaluation: methodical instructions for practical classes for students of the third year of medical faculty (specialty “medical psychology”). Chernivtsi, 2008. 19 p. (in Ukrainian).
10.    Verevina M.L., Rusakov N.V., Zhukova T.V, Gruzdeva O.A. Assessment of the incidence of the population, depending on living conditions. Gigiyena i sanitariya. 2010. Iss. 1. P. 21–25. (in Russian)
11.    Bolshakov A.M., Krutko V.N. Integral health indicators and complex systems for their evaluation. Gigiyena i sanitariya. 2011. Iss. 6. P. 51 52 (in Russian).
12.    Medic V.A., Tokmachev M.S. Manual on Health and Health Statistics. Moscow: Meditsina, 2009. 527 p. (in Russian).
13.    Shekera O.G. Health: Basic terms and indicators. Zdorovʺya suspilʹstva. 2011. Iss. 1. P. 26–31 (in Russian).
14.    Krivova O.A., Kozak L.M. Comprehensive assessment of regional demographic development. Kibernetika i vyčislitel’naâ tehnika. 2015. Iss. 182. pp. 70–84. (in Russian).
15.    Rogozinskaya N.S., Kozak L.M. Mathematical models for the dynamics of statistical indicators for the study of the health status of the population in terms of cancer incidence. Kibernetika i vyčislitel’naâ tehnika. 2011, Iss 166. P. 85–96. (in Russian).
16.    GOST 2874-82 Drinking water. Hygienic requirements and quality control. — Enter. 85-01-01. Moscow: Izdatel’stvo standartov, 1985. 6 p. (in Russian).
17.    Turbinsky V.V., Maslyuk A.I. The risk to the public health of the chemical composition of drinking water. Hygiene and sanitation. 2011. Iss. 2. P. 23–27. (in Russian).
18.    Gnevashev M.V. Statistical methods for assessing the state of water bodies on a set of ecosystem indicators for water protection purposes. Ekaterinburg, 2006. 42 p. (in Russian).
19.    Belogokrov V.P., Lozannsky V.R., Pesina S.A. Application of generalized indicators for assessing the level of contaminated water bodies. Integrated assessment of surface water quality. StPb .: Gidrometeoizdat, 2001. 34 p. (in Russian).
20.    Index of atmospheric pollution (IZA) URL: http://moreprom.ru/article.php?id=56. [Last accessed: 08.06.2018] (in Russian).
21.    Kakareka S.V. Estimation of total air pollution. Geografiya i prirodnyye resursy. 2012. Iss. 2. P. 14–20 (in Russian).
22.    Pinigin M.A. Hygienic basis for assessing the degree of air pollution. Hygiene and sanitation. 1993. Iss. 7. P. 4–8 (in Russian).
23.    Antonomov M.Yu. Mathematical processing and analysis of medico-biological data 2 ed. — Kiev: MEDC “Medinform”, 2018. 579 p. (in Russian)
24.    Saati T.L. Adoption of decisions. The method of analyzing hierarchies. Moscow: Radio i svyaz’, 1989. 316 p. (in Russian).

Resieved 11.06.2018

admin

Issue 4 (190), article 4

DOI:https://doi.org/10.15407/kvt190.04.056

Kibern. vyčisl. teh., 2017, Issue 4 (190), pp.

Antomonov M.Y.1, D. Biol. Sci., Professor,
Chief Researcher, Laboratory of Epidemiological Research and Medical Informatics
e-mail: antomonov@ukr.net
Shevchenko A.A.2, Professor,
Head of Research Center of State University “Dnipropetrovsk Medical Academy of Ministry of Health of Ukraine”
e-mail: toxysan@ukr.net
Kulagin A.A.3, Ph.D (Med. Sci.),
Professor of the Department of Hygiene and Ecology
e-mail: kulagin111188@ukr.net
1O.M. Marzeev Institute for Public Health, NAMS of Ukraine,
Ukraine, 02660 Kyiv-94, Popudrenko Str.
2Research Center of State University “Dnipropetrovsk Medical Academy of Ministry of Health of Ukraine”,
Ukraine, 49027, Dnipro, Vernadsky Str., 9
3Dnipropetrovsk Medical Academy of the Ministry of Health of Ukraine
Ukraine, 49027, Dnipro, Soborna Square, 4

ALGORITHMS OF MULTIFACTORIAL REGRESSION MODELING IN ECOLOGICAL AND HYGIENIC STUDIES

Introduction. One of the most urgent problems of environmental health is soil contamination by oil and petroleum products (PP) and its impact on crop productivity and population health. The main task here is to determine the acceptable or safe concentrations of PP in the soil. However, at present time there is no unified approach of PP regulating in different countries. One possible solution of the problem is mathematical modeling of the results of experimental studies. With the help of mathematical models, it becomes possible not only to describe the investigated dependencies, but also to calculate safe levels of PP in the soil.
The purpose of the paper is to develop a methodology for constructing and using multifactor nonlinear regression models from data obtained in a real ecological and biological experiment.
Results. The article presents the results of an experimental study of the influence of one type of petroleum products — diesel fuel, when it enters the soil to germinate one of the most important crops — wheat. It is shown that the mathematical model describing the suppression of wheat growth should be a multifactorial function (“time — concentration — effect”), which has a nonlinear character. An algorithm for constructing multifactorial nonlinear regression models is proposed. On the basis of experimental data, an adequate multifactor nonlinear mathematical model was developed. This made it possible to calculate the threshold concentration of diesel fuel, which does not affect the growth of wheat.
Conclusion. On the basis of the proposed algorithm from experimental data an adequate multifactor nonlinear regression model was constructed. With the help of this model, the concentration of diesel fuel that does not cause a negative effect on the growth of wheat was calculated.
Keywords: petrolproducts, environmental contamination, threshold concentration, mathematical modeling, nonlinear multifactor regression models.

Download full text!

REFERENCES

  1. Tyuleneva V.A. Regarding oil filtration in soils / V.A. Tyuleneva, V.A. Solyanik,
    I.V. Vaskina, VS Shalugin // Bulletin of the KSPU. Issue 2. Part 2. 2006. — P. 110–112. (in Russian).
  2. Rogozina E.A. Topical issues on cleaning of oil-contaminated soils / E.A. Rogozina // Oil and Gas Geology. Theory and practice (1). 2006. — pp. 1–10. (in Russian).
  3. Bilonenko G.M. Changes in soil fertility under hydrocarbon contamination /
    G.M. Bilonenko // Bulletin of Agrarian Science. 2002 — No. 10 — P. 52–54.
    (in Ukrainian).
  4. Abramov Yu.A. Emergency monitoring / Yu.A. Abramov, E.N. Grinchenko,
    A.Yu. Kirochkin and other // X: AGZU. 2005. — p. 530. (in Ukrainian).
  5. Boychenko S.V. Rational use of hydrocarbon fuels / S.V. Boychenko // Monograph. — M.: NAU. 2001. — P. 216. (in Ukrainian).
  6. Oborin A.A. Oil-contaminated biogeocinoses / A.A. Oborin // Monograph. – Ural Branch of the Russian Academy of Sciences. — Perm: Izvestia Perm. State Tech University 2008. — p. 511. (in Russian).
  7. Shevchenko A.A. The Study of petroleum stability of Petroleum Products in black-soil / A.A. Shevchenko, A.A. Kulagin // Materials of a Scientific-Practical Conference with International Participation [“Preventive Medicine: Achieving the Present and Looking to the Future”], Dnipropetrovsk, May 19–20, 2016). — Dnipropetrovsk, 2016. —
    P. 189–190. (in Ukrainian).
  8. Solntseva N.P. Oil production and geochemistry of natural landscapes / N.P. Solntseva // M., MTU. 1998. — p. 405. (in Russian).
  9. On approval of maximum permissible concentrations of petroleum products in land (including soils) for various land categories / Ministry of Health of the Republic of Belarus. 2012. — No. 17/1. (in Russian).
  10. Measures to rehabilitate soils contaminated by oil and petroleum products should be designated concerning sanitary and hygienic norms and environmental conditions assessment [Electronic resource] Access mode: http://ekologprom.com/osnovi-prirodooblastuvannya-ta-zaxistu-navkolishnogo- seredovishha / 92-zaxodi-z-rekultivacii-gruntiv-zabrudnenix-naftoyu.html. (in Ukrainian).
  11. Procedure for determining the size of damage from pollution of land with chemical substances (approved by Roskomzem on November 10, 1993 and the Ministry of Natural Resources of the Russian Federation on November 18, 1993). (in Russian).
  12. Methodology of damage determination caused by pollution and clogging of land resources in consequence of violations of environmental legislation / Ministry of Environmental Protection and Nuclear Safety, Kyiv, 1998. (in Ukrainian).
  13. Goncharuk E.I. Hygienic valuation of chemical substances in soil / E.I. Goncharuk,
    I.G. Sidorenko – M.: “Medicine” 1986, — 320 p. (in Russian).
  14. Use of odds ratio or relative risk to measure a treatment effect in clinical trials with multiple correlated binary outcomes: data from NINDS t-PA stroke trial M. Lu,
    B.C. Tilley // Statist. Med., 2001. — Vol.20 — P. 1891–1901. doi: 10.1002 / sim.841
  15. Stability analysis and optimal control of a hand-foot-mouth disease (HFMD) model / Jun-Yuan Yang, Yuming Chen, Feng-Qin Zhang // Journal of Applied Mathematics and Computing. — 2013. — Vol. 41 — T. 1-2. — P. 99–117
  16. Analysis of a time-delayed mathematical model for solid avascular tumor growth under the action of external inhibitors / Shihe Xu, Yinhui Chen, Meng Bai // Journal of
    Applied Mathematics and Computing. — 2016. — Vol. 52. — T. — p. 403–415.
  17. The Foundations of a Unified Approach to Mathematical Modeling of Angiogenesis /
    M. Hubbard, P. F. Jones, B. D. Sleeman // International Journal of Advances in Engineering Sciences and Applied Mathematics. — 2009–1: 43.
  18. Optimal control of mathematical models for radiotherapy of gliomas: the scalar case. /
    E. Fernández-Cara, L. Prouvée // Computational and Applied Mathematics. — 2016. Vol. 69 — P 1–18.
  19. Introduction to the Use of Regression Models in Epidemiology Ralf Bender, Cancer Epidemiology, v1, 2009, pp. 179–195.
  20. Qualitative analysis of a SIR epidemic model with a saturated treatment rate / Zhang Zhonghua, Suo Yaohong // Journal of Applied Mathematics and Computing. 2010 34,
    T. 1–2, P. 177–194.
  21. Regression models for multiple outcomes in large epidemiological studies. S. B. Bull. // static Med., 1998. — Vol. 17. P. 2179–2197.
  22. Stochastic Analysis of an Influenza Epidemic Model. / M. Waleed, M. Imran, A. Khan // International Journal of Applied and Computational Mathematics. — 2017 — Vol. 3. —T.2. — P. 425–443.
  23. A nonlinear population model of diabetes mellitus / A. Boutayeb, A. Chetouani,
    A. Achouyab, E. H. Twizell // Journal of Applied Mathematics and Computing, 2006 —Vol. 21. — t 1–2. — p. 127–139.
  24. Biostatistics for Medical and Biomedical Practitioners, 1st Edition / J. Hoffman //
    Academic Press, 2015 — 770p.
  25. Biomedical Informatics. Computer Application in Health Care and Biomedicine, 4th edition / Editors: E. H. Shortliffe, J. J. Cimino // NY, Springer, 2014, 965 p.
  26. Computational and Statistical Methods for Analyzing Big Data with Applications in 1st Edition / S. L. James, M. Zongyuan, G. Y. Xie // Academic Press / — 2015. — 206 p.
  27. Theory and Methods of Statistics .1st Edition. / P.K. Bhattacharya, P. Burman // Academic Press. — 2016. — 544 p.
  28. Methodological recommendations for the hygienic substantiation of the MPC of chemical substances in soil No. 2609-82. — (Effective from 05.08.1982). — Moscow: Ministry of Foreign Affairs of the USSR, 1982. — 57 p. (in Russian).

Reseived 01.08.2017

admin

Issue 185, article 7

DOI:https://doi.org/10.15407/kvt185.03.077

KVT, 2016, Issue 185, pp.77-89

UDC 519.876.5:615.33

MODELING ANTIMICROBIAL ACTIVITY ANTIBIOTIC “CEFAZOLIN” AND SILVER NANOPARTICLES

Antomonov M.Y., Romanenko L.I.

Marzeev’s Public Health Institute of the National Medical Academy of Science of Ukraine of Ukraine, Kiev, Ukraine

antomonov@gmail.com , Luda_romanenko@ukr.net

Introduction. The creation of combined drugs, which are used in the composition of nanoparticles (NPs) is actively developing. The most relevant developments include the NPs composition and antibiotics. Mathematical models of the process under investigation lead to a theoretical understanding of this phenomenon, allow us to describe the process in the form of mathematical functions, make it possible to predict the outcome, to analyze the properties of the model and to obtain new data, without undue experimentation.

The purpose of the article is to determine the characteristics of antimicrobial activity of Ag NP in combination with an antibiotic “Cefazolin” with the help of mathematical models of their actions (“concentration – time – effect”).

Results. Antimicrobial properties of the composite material were considered, which is called the “Silver Shield-1000”, and which consists of antibiotic “Cefazolin” and NPs Ag. Isolated action “Silver Shield-1000” was considered at the first stage of the experiment. For this purpose dilution nanosilver 15,0; 7,5; 3,75; 1,875 (g/cm3), and the holding time was 5, 60, 120, 180 minutes. Based on experimental plots and the general theoretical ideas about the dynamics of the process of the withering away of microbes, mathematical model, y(t) has been selected as the exponential function y = y0 (C) exp[-α(C) T]. The values of the model parameters were calculated based on the original data using numerical methods (Levenberg-Marquardt) and software STATISTICA 10,0. The final model organisms, dying under the influence of the “Silver Shield-1000” had the following form: y = y0 exp(-btC)= 59,74exp(-0,0013 TС). Using this model, the expected value of the concentration С* at which should occur almost complete disappearance of microorganisms immediately after the start of the exposure (С* = 73,15 g/cm3) was calculated. An additional experiment was performed, which confirmed this value that verifies the adequacy of the model as a whole. In the second phase of researching it was experimentally investigated the combined effect of “Silver Shield” — 1000 (3.75 g/cm3) and the antibiotic “Cefazolin” on the death of microorganisms. A mathematical model “concentration — time — effect” for the composite: y = 60,098exp(-0,118 TC) and an antibiotic: y = 60,098exp(-0,012 TC). This made it possible to calculate the efficiency of the composite action compared with isolated action of an antibiotic (k = 9.72). Furthermore, it was shown that the composite exhibits have a much greater effect than the sum of the isolated antibiotic action “Cefazolin” and “Silver shield —1000”. It is possible to determine the nature of such action as potentiation.

Conclusion. Mathematical modeling of the results of an experimental study of the antimicrobial activity of the antibiotic “Cefazolin” in combination with nanoparticles of silver allowed the calculation of the quantitative characteristics of the effectiveness of the active ingredients and performed a meaningful forecast of their action.

Keywords: silver nanoparticles, antibiotic “Cefazolin”, death of microorganisms, mathematical model.

Download full text (ru)!

References:

  1. Nanometaly: the state of current research and use in biology, medicine and veterinary / V.F. Shatorna V.I. Harets V.V. Krutenko et al. // Herald problems of biology and medicine. — 2012. — Vol. 3. — T. 2. — P. 29–33.
  2. Nanoparticles of metals: methods of preparation, physical and chemical properties, methods of research and evaluation of the toxicity // Suchasnі problemi toksikologії. — 2011. — № 3. — P. 5–13.
  3. I.S. Chekman Nanopharmacology: state and prospects of research // Journal of Pharmacology and Pharmacy. — 2007. — № 11. — P. 7–10.
  4. Biogenic synthesis of silver nanoparticles and their synergistic effect with antibiotics: a study against gram-positive bacteria / A.M. Fayas, K. Balaji, M. Girial, et al. // Nanomedicine. — 2010. — Vol. 6(1). — P. 103–109.
  5. Synergistic antibacterial effects of beta-lactam antibiotic combined with silver nanoparticles / P. Li, J. Li, C. Wu et al. // Nanotechnology. — 2005. — Vol. 16(9). — P. 1912–1917.
  6. Coping with antibiotic resistance: combining nanoparticles with antibiotics and other antimicrobial agents / A.M. Allahvcrdiyev, K.V. Kon, E.S. Abamor et al. // Expert Rev Anti Infect Ther. — 2011. — Vol. 9(11). — P. 1035–1052.
  7. Preparations silver: yesterday, today and tomorrow / O.B. Shcherbakov, G.I. Korczak, I.N. Skorokhod et al. // Pharmaceutical Journal. — 2006. — № 5.
  8. Syntesis and effect of silver nanoparticles on the antibacterial activity of different antibiotics against Stafphylococcus aureus and Escherichia coli / A.R. Shahverdi, A. Fakhimi, H.R. Shahverdi, S. Minaian // Nanomedicine. — 2007. — Vol. 3. — P. 168–171.
  9. M.Y. Antomonov The mathematical processing and analysis of medical and biological data. — Kiev: Publishing house “Malii Druk”, 2006. — 558p.
  10. Dudko DA, Sadohin VP, Kisterski AL-stage method of preparation of highly concentrated suspensions of nanoscale particles of conductive materials based on water-soluble and water-insoluble liquids and device for its implementation UA 80513 C2 25.09.2007

Received 16.06.16

admin

Issue 182, article 6

DOI:https://doi.org/10.15407/kvt182.02.066

Kibern. vyčisl. teh., 2015, Issue 182, pp.

Antomonov M.Y.

State Institution “O.N. Marzeev Institute for Hygiene and Medical Ecology
of NAMS of Ukraine” (Kiev)

THE METHOD OF DETERMINATION OF ENVIRONMENTAL FACTORS JOINT IMPACT IN EPIDEMIOLOGICAL STUDIES FOR BINARY DATA

Introduction. Modern approaches for data analysis combine classical methods and focused on their practical application. Sometimes the information is presented in the form of qualitative characteristics that are characterize the contamination of the research object. Such binary variables are easily transformed into a probability (in percent), so the task description of results performed using probability theory.
The purpose of the article is to develop such a common method forcalculation joint action of the factors that would allow to operate with qualitative (binary) information and would use techniques and formulas of probability theory
Results. A careful analysis was carried out for the existing approaches in the medical and environmental studies for calculating the effect of the joint action of the factors. It was evaluated disadvantages of these approaches that implemented in the theory of probability and mathematical statistics. The article proposes an original method of calculating the combined effect of the factors that allows you to work with the information expressed in binary form. The final expression was designed by using approach of formal logic and probability theory.
Conclusions. It is shown that the known methods of probability theory cannot be adequately used to evaluate the combined effect of the factors. The original method of calculating the probability of the joint action of factors that take into account their possible connection is described.
Keywords: qualitative data, binary variables, joint effect of the factors, the probability of independent and interdependent events.

Download full text (ru)!

References

1 Duke V. A. Samoilenko A. P. Data Mining. Training — SPb, 2001. — 368p.

2 David W. Hosmer, Stanley Lemeshow Applied Logistic Regression, 2nd ed. — New York, Chichester, Wiley. 2002. — 383p.

3 Nasledov A. N. SPSS 19: Professional statistical analysis. — SPb, 2011. — 400 p.

4 Greenacre M. Correspondence Analysis in Practice, 2nd ed. — London: Chapman & Hall / CRC — 2007. — 280 p.

5 Ritchie M. D., Hahn L. W., Roodi N., Bailey L. R., Dupont W. D., Parl F. F., Moore J. H. Multifactor-dimensionality reduction reveals high-order interactions among estrogenmetabolism genes in sporadic breast cancer. Am. J. Hum. Genet. 2001 Jul; 69 (1): 138-47. https://doi.org/10.1086/321276

6 Hahn L. W., Ritchie M. D., Moore J. H. Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions//Bioinformatics. 2003 Feb 12; 19 (3): 376-82 https://doi.org/10.1093/bioinformatics/btf869

7 Orlov A. I. Applied Statistics — M .: Publisher “Exam”, 2004. — 656 p.

8 Antomonov M. Y. Mathematical processing and analysis of medical-biological data. — Kiev: Publishing house “Malii Druk”, 2006. — 558p.

9 Gaydyshev I. Analysis and data processing — St. Petersburg, 2001. — 750p.

10 Wentzel E. S. Probability 10th ed., Sr — M.: “Academy”, 2005. — 576 p.

11 Gnedenko B. V., Khinchin A. Y. An elementary introduction to the theory of probability, 1970. — 168p.

12 Novikov P. S. Elements of mathematical logic. 2nd ed. — M .: Nauka, 1973 — 400 p.

Received 02.06.2015

admin

ISSUE 179, article 7

DOI: https://doi.org/10.15407/kvt179.01.081

Kibern. vyčisl. teh., 2015, Issue 179, pp 81-92.

Pashinskaia Svetlana L., Junior researcher of the Medical Informatics Department of the Marzeev’s Institute of Hygiene and Medical Ecology of the National Medical Academy of Sciences of Ukraine, Popudrenko st., 50, Kiev, 02094, Ukraine, email: pashynska_sv@gmail.com

Antomonov Mikhail Yu., Dr (Biology), Prof., Head of Medical Informatics Department of Marzeev’s Institute of Hygiene and Medical Ecology of the National Medical Academy of Sciences of Ukraine, Popudrenko st., 50. Kiev, 02094, Ukraine, email: antomonov@gmail.com

INVERSE PROBLEMS OF INTEGRATED EVALUATION: IDENTIFICATION OF CRITICAL COMPONENTS OF HEALTH AND ECOLOGICAL SITUATION

Introduction. Integrated assessment of environmental quality in medical ecological research used different mathematical structures that are often weighted sum of expression of all reported hazards. The problem of optimal formation of integrated indicators is the direct problem of integral evaluation.

The purpose of paper is to develop methods, algorithms, computational formulas and software implementation for solving the inverse problem of the integral evaluation — identifying destabilizing factors in the assessment of ecological and hygienic objects.

Results. Realization of this aim requires the formulation and solution of problems phased: express processing of data array; the selection of informative features; construction of an integrated evaluation.

Algorithm of construction integrated evaluation is implemented as follows: the calculation of the normalized equivalents of selected indicators; calculation of weighted average grade of the objects on the normalized equivalents; calculation shifted integrated evaluation, as the square root of the product of the minimum and weighted average; comparison of average and shifted integrated evaluation; identification of critical elements.

The paper presents an automated technology analysis and evaluation of the primary indicators in order to optimize their list to calculate the integral evaluation. The technology allows working with arrays having outliers and missing data. We consider a phased construction of an integrated assessment. The nonlinear algorithm of integration indicator formation and the method for identifying critical elements were developed.

Conclusions. The proposed technology allows to quickly implementing the processing of the data array, to bring it to a format suitable for further, more detailed analysis and to form an integrated assessment. The results of processing may be performed within the environmental and medical objects; identify objects with the most adverse environmental conditions and disease. Calculation of displaced integral indicators shows destabilizing elements in the system of indicators.

Keywords: integrated evaluation, ecological and hygienic objects, medical and ecological research.

Download full text (ru)!

References

  1. Lemeshko B.Yu., Rogozhnikov A.P. Investigation of the features and power of some of the criteria of normality. Metrologiya, 2009, no 4, pp. 3–24 (in Russian).
  2. Vasil’yev V.I.., Shevchenko A.I. Recovery of missing data in empirical tables. Iskusstvennyy intellect, 2003, no 3, pp. 317–324 (in Russian).
  3. Zloba Ye., Yatskiv I. Statistical methods for recovery of missing data. Computer Modeling & New Technologies, 2002. Vol. 6. no 1. pp. 51–61 (in Russian).
  4. Bakumenko L.P., Korotkov P.A. Integral assessment of the quality and environmental sustainability of the region (on the example of the Republic of Mari El). Prikladnaya ekonometrika. 2008, no 1(9), pp. 73–92 (in Russian).
  5. Pavlov S. B. Ekologіchny rizik for Health Protection of the population. Meditsinskiye issledovaniya, 2001. T. 1, issue 1, pp. 16–19 (in Ukrainian).
  6. Antomonov M.Yu. Mathematical processing and analysis of medical and biological data. Kiev: Malyi druk, 2006. 558 p (in Russian).
  7. Shuyskiy V.F., Zantsinskaya T.P., Petrov D.S. Quantification and valuation of complex anthropogenic impact on macrozoobenthos. Sb. nauch. tr. GosNIORKH, issue 326. 2000, pp. 137–144 (in Russian).
  8. Faynzilberg L.S. Plausible, but wrong decisions in the construction of diagnostic rules. Materialy vosmoy distantsionnoy nauchno-prakticheskoy konferentsii s mezhdunarodnym uchastiyem. «Sistemy podderzhki prinyatiya resheniy. Teoriya i praktika. SPPR ’2012». Kiev, IPMMS NAN Ukrainy, 2012, pp. 31–34 (in Russian).

Received 07.11.2014