| Autor: |
Korenevskiy, Nikolay A., Al-Kasasbeh, Riad Taha, Shaqadan, Ashraf, Myasoedova, Marina Anatolevna, Al-Qodah, Zakaria, Rodionova, Sofia N., Eltous, Yousif, Filist, Sergey, Maksim, Ilyash |
| Zdroj: |
International Journal of Systems Assurance Engineering & Management; Oct2024, Vol. 15 Issue 10, p4853-4873, 21p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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