Assessment of the role of various factors in the development of arterial hypertension in workers of chemical enterprises by applying machine learning
https://doi.org/10.25789/YMJ.2025.90.16
Abstract
The article examines the contribution of production-related and non-production risk factors to the development of arterial hypertension in workers in ethylbenzene-styrene (EBS) and ethylene-propylene (EP) chemical industries, which differ in the degree of automation of the technological process, using the gradient boosting method.
The factors that have the greatest impact on the prognosis of arterial hypertension development have been identified. For EBS production workers, the major factors influencing AH risk were work experience, age, and smoking. For the Ethylene-Propylene (EP) operators, the key factors were work experience, smoking, and LDL levels.
Taking into account the identified risk factors, targeted measures have been developed to minimize them.
About the Authors
N. A. Muldasheva
ФБУН «Уфимский научно-исследовательский институт медицины труда и экологии человека»
Russian Federation
Russian Federation
I. I. Zaydullin
ФБУН «Уфимский научно-исследовательский институт медицины труда и экологии человека»
Russian Federation
Russian Federation
D. O. Karimov
ФБУН «Уфимский научно-исследовательский институт медицины труда и экологии человека»
Russian Federation
Russian Federation
L. K. Karimova
ФБУН «Уфимский научно-исследовательский институт медицины труда и экологии человека»
Russian Federation
Russian Federation
I. V. Shapoval
ФБУН «Уфимский научно-исследовательский институт медицины труда и экологии человека»
Russian Federation
Russian Federation
Z. F. Gimaeva
ФБУН «Уфимский научно-исследовательский институт медицины труда и экологии человека»
Russian Federation
Russian Federation
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Review
For citations:
Muldasheva N.A., Zaydullin I.I., Karimov D.O., Karimova L.K., Shapoval I.V., Gimaeva Z.F. Assessment of the role of various factors in the development of arterial hypertension in workers of chemical enterprises by applying machine learning. Yakut Medical Journal. 2025;(2):75-79. https://doi.org/10.25789/YMJ.2025.90.16
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