Synthesis of Models for Predicting and Diagnosing Occupational Diseases Based on Hybrid Fuzzy Technology-Reference-Cited by-同舟云学术

Synthesis of Models for Predicting and Diagnosing Occupational Diseases Based on Hybrid Fuzzy Technology

Published:2024-01-27 Issue:3 Volume:13 Page:102-121
ISSN:2223-1536
Container-title:Proceedings of the Southwest State University. Series: IT Management, Computer Science, Computer Engineering. Medical Equipment Engineering
language:
Short-container-title:jour

Author:

Safronov R. I.¹^ORCID,Razumova K. V.²^ORCID,Rybakov А. Y.²^ORCID,Lyakh А. V.²^ORCID

Affiliation:

1. Kursk State Agricultural Academy Named After I. I. Ivanov

2. Southwest State University

Abstract

The purpose of research is to develop a method for synthesizing models for predicting and diagnosing occupational diseases based on hybrid fuzzy technology, providing an increase in the quality of decision-making in occupational pathology.Methods. It is established that most of the problems related to the topic under study (forecasting, early diagnosis, assessment of the severity and dynamics of the development of professional diseases) belong to the class of poorly formalized problems with fuzzy and incomplete data structure, which are recommended to be solved using the methodology of synthesis of hybrid fuzzy solving rules based on on the interaction of the natural intelligence of doctors and a cognitive engineer with artificial hybrid intelligence. Using the chosen mathematical apparatus, a method for the synthesis of fuzzy models of forecasting and diagnosis of occupational diseases is proposed.Results. As a concrete example, the problem of predicting and diagnosing ischemic heart disease (CHD) in electric train drivers has been solved with the allocation of such classes of conditions as: "healthy and the appearance of CHD is not expected"; "healthy, but the appearance of CHD is expected after the predicted time"; "early stage of CHD"; "CHD disease has been detected". As a result of expert evaluation, it was shown that the confidence in the correct classification is at the level of 0.9. The same result was confirmed by the results of statistical tests on representative control samples in terms of diagnostic sensitivity and specificity.Conclusion. The proposed method of synthesis of hybrid fuzzy models makes it possible to synthesize hybrid decision rules that improve the quality of prediction and early diagnosis of the studied class of diseases both in the presence of training samples and in their absence by compensating for the lack of statistical material by methods of formalization of clinical thinking. As a concrete example, the problem of predicting and diagnosing ischemic heart disease in electric train drivers has been solved. It is shown that the confidence in the correct classification is at the level of 0.9, which allows us to recommend the results obtained for practical use in occupational pathology.

Publisher

Southwest State University

Reference35 articles.

1. Korenevskij N. A., Shutkin A. N., Gorbatenko S. A., Serebrovskij V. I. Ocenka i upravlenie sostoyaniya zdorov'ya obuchayushchihsya na osnove gibridnyh intellektual'nyh tekhnologij [Assessment and management of the health status of students based on hybrid intellectual technologies]. Staryj Oskol, Tonkie naukoemkie texnologii Publ., 2016. 472 p.

2. Korenevskij N. A., Serebrovskij V. I., Stepashov R. V., Govoruxina T. N. Ispol'zovanie tekhnologii myagkih vychislenij dlya prognozirovaniya i diagnostiki professional'nyh zabolevanij rabotnikov agropromyshlennogo kompleksa [Using Soft Computing Technology to Predict and Diagnose Occupational Diseases of Agro-Industrial Workers]. Kursk, Kursk State Agricultural Academy Publ., 2016. 224 p.

3. Korenevskij N. A. Metod sinteza geterogennyh nechetkih pravil dlya analiza i upravleniya sostoyaniem biotekhnicheskih sistem [Method for the synthesis of heterogeneous fuzzy rules for the analysis and control of the state of biotechnical systems]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universitetyu. Seriya: Upravlenie, vychislitel'naya tekhnika, informatika. Medicinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering, 2013, no. 2, pp. 99–103.

4. Serebrovskij V. I., Myasoedova M. A., Serebrovskij V. V., eds. Prognozirovanie i rannyaya diagnostika professional'nyh zabolevanij v elektroenergetike na osnove metodologii sinteza gibridnyh nechetkih reshayushchih pravil [Forecasting and early diagnosis of occupational diseases in the electric power industry based on the methodology for the synthesis of hybrid fuzzy decision rules]. Kursk, Kursk State Agricultural Academy Publ., 2019. 285 p.

5. Korenevskij N. A., Bashir A. S., Gorbatenko S. A. Sintez gibridnyh nechetkih pravil dlya prognozirovaniya, ocenki i upravleniya sostoyaniem zdorov'ya v ekologi-cheski neblagopriyatnyh regionah [Synthesis of hybrid fuzzy rules for forecasting, assessment and management of health in ecologically unfavorable regions]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universitetyu. Seriya: Upravlenie, vychislitel'naya tekhnika, informatika. Medicinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering, 2013, no. 4, pp. 69–73.