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Prediction of health impacts of exposure to electromagnetic field on the immunity system of power plants workers using fuzzy decision-making rules

Author

Listed:
  • Nikolay A. Korenevskiy

    (South-West State University)

  • Riad Taha Al-Kasasbeh

    (The University of Jordan)

  • Ashraf Shaqadan

    (Zarqa University)

  • Marina Anatolevna Myasoedova

    (Kursk State Agricultural Academy)

  • Zakaria Al-Qodah

    (Al Balqa’a Applied University)

  • Sofia N. Rodionova

    (South-West State University)

  • Yousif Eltous

    (Al Balqa’a Applied University)

  • Sergey Filist

    (South-West State University)

  • Ilyash Maksim

    (ITMO University)

Abstract

This study aims to enhance health assessments in environments with industrial risk factors by incorporating oxidative status indicators, such as lipid peroxidation levels and antioxidant activity, into prognostic and diagnostic models. A novel approach was developed to quantitatively evaluate the body’s protection level by synthesizing hybrid fuzzy decision rules that integrate oxidative status indicators. The methodology was validated through a case study focusing on predicting ischemic heart disease in locomotive crew drivers, who are at high risk for disability and mortality due to their occupational environment. The incorporation of oxidative status into prognostic decision rules significantly improved the accuracy and efficiency of disease prediction. In particular, fuzzy mathematical models were also developed to predict and diagnose immune system diseases in electric power industry workers exposed to electromagnetic fields and other risk factors. Statistical tests revealed that the decision rules achieved a prediction accuracy greater than 0.85, with early-stage detection accuracy reaching 0.95. These findings provide occupational pathology specialists with a valuable tool for enhancing the precision of disease prediction and diagnosis in industrial settings. The integration of oxidative status indicators into prognostic models offers a promising approach to improving health outcomes for workers exposed to industrial risk factors.

Suggested Citation

  • Nikolay A. Korenevskiy & Riad Taha Al-Kasasbeh & Ashraf Shaqadan & Marina Anatolevna Myasoedova & Zakaria Al-Qodah & Sofia N. Rodionova & Yousif Eltous & Sergey Filist & Ilyash Maksim, 2024. "Prediction of health impacts of exposure to electromagnetic field on the immunity system of power plants workers using fuzzy decision-making rules," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(10), pages 4853-4873, October.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:10:d:10.1007_s13198-024-02489-3
    DOI: 10.1007/s13198-024-02489-3
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    References listed on IDEAS

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    1. Liesbeth Claassen & Diana van Dongen & Danielle R.M. Timmermans, 2017. "Improving lay understanding of exposure to electromagnetic fields; the effect of information on perception of and responses to risk," Journal of Risk Research, Taylor & Francis Journals, vol. 20(9), pages 1115-1131, September.
    2. Alberto Modenese & Fabriziomaria Gobba, 2021. "Occupational Exposure to Electromagnetic Fields and Health Surveillance according to the European Directive 2013/35/EU," IJERPH, MDPI, vol. 18(4), pages 1-12, February.
    3. ASGHAR, Zahid, 2008. "Energy–Gdp Relationship: A Causal Analysis For The Five Countries Of South Asia," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 8(1), pages 167-180.
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