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

Nikolay A. Korenevskiy, Riad Taha Al-Kasasbeh (), Ashraf Shaqadan, Marina Anatolevna Myasoedova, Zakaria Al-Qodah, Sofia N. Rodionova, Yousif Eltous, Sergey Filist and Ilyash Maksim
Additional contact information
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

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 10, No 11, 4853-4873

Abstract: 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.

Keywords: Prediction; Industrial electromagnetic fields; Immune system; Fuzzy logic; Decision-making; Bioactive points; Health risk (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-024-02489-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:15:y:2024:i:10:d:10.1007_s13198-024-02489-3

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-024-02489-3

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().