Journal "Software Engineering"
a journal on theoretical and applied science and technology
ISSN 2220-3397

Issue N1 2025 year

DOI: 10.17587/prin.17.49-56
Impact of Helioclimatic Factors on Human Health: An Expert System for Risk Prediction
R. A. Burnashev, PhD in Technical Sciences, Associate Professor, ORCID: 0000-0002-1057-0328, r.burnashev@inbox.ru, E. A. Barov, M. Sc., ORCID: 0000-0001-7761-341X, evgeniy.barov@vk.com, Institute of Computational Mathematics and Information Technologies, Kazan (Volga Region) Federal University, Kazan, 420008, Russian Federation
Corresponding author: Rustam A. Burnashev, PhD in Technical Sciences, Associate Professor, Institute of Computational Mathematics and Information Technologies, Kazan (Volga Region) Federal University, Kazan, 420008, Russian Federation, E-mail: r.burnashev@inbox.ru
Received on June 24, 2025
Accepted on August 01, 2025

This article presents the results of research aimed at developing a hybrid health risk assessment methodology that combines copula models (vine copulas) and fuzzy logic. Based on data from the Republic of Tatarstan for2000—2021, dependencies between demographic, climatic, and heliogeophysical factors were identified. Using copula modeling, linguistic variables were determined and integrated into a prototype medical expert system for risk assessment. The developed approach enables the analysis of complex dependencies and can serve as a mathematical basis for healthcare decision support systems.

Keywords: copula models, expert system, fuzzy logic, risk assessment, heliogeophysical factors, mortality, time series analysis, public health, environmental health, predictive modeling
pp. 49—56
For citation:
Burnashev R. A., Barov E. A. Impact of Helioclimatic Factors on Human Health: An Expert System for Risk Prediction, Programmnaya Ingeneria, 2026, vol. 17, no. 1, pp. 49—56. DOI: 10.17587/prin.17.49-56 (in Russian).
References:
  1. Lobanov V. A., Toshchakova G. G. Features and causes of modern climate change in Russia, Geograficheskiy vestnik, 2016, no. 3 (38), pp. 79—89. DOI: 10.17072/2079-7877-2016-3-79-89 (in Russian).
  2. Shartova N. V., Shaposhnikov D. A., Konstantinov P. I., Revich B. A. Air temperature and mortality: a study of threshold values of heat and population sensitivity on the example of Rostov-on-Don, Fundamental'naya i prikladnaya klimatologiya, 2019, vol. 2, pp. 66—94. DOI: 10.21513/2410-8758-2019-2-66-94 (in Russian).
  3. Grigoryeva E. A. Population mortality at extreme temperatures: forecast methodology and assessment results, Gigiena i sanitariya, 2019, vol. 98, no. 11, pp. 1279—1284. DOI: 10.18821/0016­9900-2019-98-11-1279-1284 (in Russian).
  4. Tolstov P. V., Kalyagin A. N., Tatarinova M. B. The influence of heliogeophysical and natural-climatic factors on the cardiovascular system (literature review), Kardiovaskulyarnaya terapiya i profilaktika, 2023, vol. 22, no. 8, pp. 92—102. DOI: 10.15829/1728­8800-2023-3599 (in Russian).
  5. Roth G. A., Abate D., Abate K. H. et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980—2017: a systematic analysis for the Global Burden of Disease Study 2017, The Lancet, 2018, vol. 392, no. 10159, pp. 1736—1788. DOI: 10.1016/s0140-6736(18)32203-7.
  6. Taseiko O. V., Chernykh D. A. Assessing the impact of environmental factors on mortality rates in older age groups on the example of Krasnoyarsk, Problemy upravleniya, 2021, no. 5, pp. 60—69. DOI: 10.25728/pu.2021.5.6 (in Russian).
  7. Hall M., Friel N. Mortality Projections using Generalized Additive Models with applications to annuity values for the Irish population, Annals of actuarial science, 2010, vol. 5, no. 1, pp. 19—32. DOI: 10.1017/s1748499510000011.
  8. Ohashi Yu., Ihara T., Oka K. et al. Machine learning analysis and risk prediction of weather-sensitive mortality related to cardiovascular disease during summer in Tokyo, Japan, Scientific Reports, 2023, vol. 13, no. 1, article 17020. DOI: 10.1038/s41598-023-44181-9.
  9. Kendys A. M. Application of copula models in time series analysis, Theory of Probability, Mathematical Statistics and Applications: Proceedings of the International Scientific Conference, Minsk, April 22—24, 2024, Minsk, Belarusian State University, 2024, pp. 84—91 (in Russian).
  10. Penikas G. I., Simakova V. B. Interest rate risk management based on copula-GARCH models, Prikladnaya ekonometrika, 2009, vol. 1(13), pp. 3—36 (in Russian).
  11. Pramanik P. Dependence on Tail Copula, /, 2024, vol. 7, no. 2, pp. 127—152. DOI: 10.3390/j7020008.
  12. Lefohn A. S., Malley C. S., Smith L. et al. Tropospheric ozone assessment report: Global ozone metrics for climate change, human health, and crop/ecosystem research, Elementa. Science of the Anthropocene, 2018, vol. 6, article 28. DOI: 10.1525/elementa.279.
  13. Petitti D. B., Hondula D. M., Yang S. et al. Multiple Trigger Points for Quantifying Heat-Health Impacts: New Evidence from a Hot Climate, Environmental Health Perspectives, 2016, vol. 124, no. 2, pp. 176—183. DOI: 10.1289/ehp.1409119.
  14. Czado C., Nagler T. Vine Copula Based Modeling, Annual Review of Statistics and Its Application, 2022, vol. 9, no. 1, pp. 453—477. DOI: 10.1146/annurev-statistics-040220-101153.
  15. D'Urso P., De Giovanni L., Vitale V. A D-vine copulabased quantile regression model with spatial dependence for COVID-19 infection rate in Italy, Spatial Statistics, 2022, vol. 47, article 100586. DOI: 10.1016/j.spasta.2021.100586.
  16. Germashev I. V., Dubovskaya V. I. Application of Fuzzy Mathematics Models for Solving Medical Diagnostics Problems, Mathematical Physics and Computer Simulation, 2021, vol. 24, no. 4, pp. 53—66. DOI: 10.15688/mpcm.jvolsu.2021.4.4 (in Russian).
  17. Nguyen T., Khosravi A., Creighton D., Nahavandi S. Medical data classification using interval type-2 fuzzy logic system and wavelets, Applied Soft Computing, 2015, vol. 30, pp. 812—822. DOI: 10.1016/j.asoc.2015.02.016.
  18. Ontiveros E., Melin P., Castillo O. Comparative study of interval Type-2 and general Type-2 fuzzy systems in medical diagnosis, Information Sciences, 2020. vol. 525, pp. 37—53. DOI: 10.1016/j.ins.2020.03.059.
  19. Kantorovich G. G. Time Series Analysis, Ekonomicheskiy Zhurnal Vysshey Shkoly Ekonomiki, 2002, vol. 6, no. 3, pp. 379—401 (in Russian).
  20. Vetrov D. P., Kropotov D. A., Ptashko N. O. An Efficient Method for Feature Selection in Linear Regression by Generalizing the Akaike Information Criterion, Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, 2009, vol. 49, no. 11, pp. 2066—2080 (in Russian).
  21. Tagirova L. F., Zubkova T. M. Development of an Expert System for Determining the Status of a Student of an Intelligent System Based on the Mamdani Fuzzy Inference Algorithm, Informatsionnye Tekhnologii, 2022, vol. 28, no. 7, pp. 359—368. DOI: 10.17587/it.28.359-368 (in Russian).
  22. de Freitas R. A. P., Marques J. R. Q., da Costa R. P. et al. Variations in Atmospheric Electromagnetism and Their Impacts on Mortality Rates in the Southern and Northeastern Regions of Brazil, Atmospheric and Climate Sciences, 2025, vol. 15, no. 1, pp. 248 —274. DOI: 10.4236/acs.2025.151012.
  23. Krylov V. V. Biological effects of geomagnetic activity: observations, experiments, and possible mechanisms, Proceedings of the I. D. Papanin Institute for Biology of Inland Waters RAS, 2018, no. 84 (87), pp. 7—38. DOI: 10.24411/0320-3557-2018-10016 (in Russian).
  24. Ziubryte G., Jarusevicius G., Landauskas M. et al. Cardiovascular System Interactions With The Local Earth Magnetic Field Fluctuations: A Cohort Study, Research Square (Research Square), Published online March 3, 2021. DOI: 10.21203/rs.3.rs-250922/v1.
  25. Gadjiev G. D. Geomagnetic storms and their impact on morbidity indicators among employees of research institutes of the Irkutsk Scientific Center of the Siberian Branch of the Russian Academy of Sciences, Siberian Medical Journal (Irkutsk), 2007, vol. 75, no. 8, pp. 59—62 (in Russian).
  26. Bazhenov A. A., Averina A. S., Prikop M. V. Influence of heliogeophysical factors on human health, Bulletin of the Eastern-Siberian Scientific Center of the Siberian Branch of the Russian Academy of Medical Sciences, 2014, no. 6 (100), pp. 125—129 (in Russian).
  27. Ziubryte G., Siauciunaite V., Jarusevicius G., McCraty R. Local earth magnetic field and ischemic heart disease: peculiarities of interconnection, Cardiovascular Disorders and Medicine, 2018, vol. 3, no. 4. DOI: 10.15761/cdm.1000177.
  28. Stoupel E. Effect of geomagnetic activity on cardiovascular parameters, Journal of Clinical and Basic Cardiology, 1999, vol. 2, no. 1, pp. 34—40.