Intelligent digitalization of cardiovascular risks
Автор: Gromov Y.Y., Gorbunov A.V., Tyutyunnik V.M.
Журнал: Cardiometry @cardiometry
Рубрика: Report
Статья в выпуске: 22, 2022 года.
Бесплатный доступ
The aim of the study was to develop a mathematical model of the risks of the cardiovascular system based on the selected factors affecting cardiovascular diseases (CVD) and to test the developed mathematical model on a sample of clinical examples. CVD risk factors was grouped by types: biological indicators (anthropometric, biochemical, morphological, physiological), disease indicators, social indicators. An assessment of the degree of risk for each of the indicators was carried out by calculating the degree of risk using the membership formula, then evaluating the hazard class (according to the degree of risk) using a logical-linguistic model and a training algorithm for the neural fuzzy classifier of the network. The correctness of the risk determination by the developed model was confirmed by the analyzed 60 verified cases of acute cerebrovascular accident (18 men and 42 women). The analysis of the test results of the constructed neuro-fuzzy classifier allows us to conclude that it works satisfactorily even when using incomplete information, which makes it possible to use it for prompt decision-making. The results of testing on clinical examples, with an acceptable level of significance of a type I error of 0.05, showed that the risk was determined correctly. The factors influencing the risk of CVD are identified and designated as the corresponding linguistic variables. A logical-linguistic model was built, from which a transition was made to a hybrid neuro-fuzzy classifier, which allows assessing the influence of the identified factors on the level of risk of CVD. As a result of approbation of the model of intellectual digitalization of risks of the cardiovascular system on real clinical examples, it was confirmed that the risk was determined correctly, which means that it is possible to assert about the prospects for introducing this model into clinical practice and guaranteeing medical specialist more accurate diagnosis and optimization of their activities.
Cardiovascular diseases, risk factors, mathematical model, hazard class, neuro-fuzzy classifier
Короткий адрес: https://sciup.org/148324644
IDR: 148324644 | DOI: 10.18137/cardiometry.2022.22.7794
Список литературы Intelligent digitalization of cardiovascular risks
- World Health Organization. Cardiovascular diseases. URL: https://www.who.int/cardiovascular_diseases/about_cvd/ru/. (In Russ.).
- Strong K, Mathers C, Epping-Jordan J, Beaglehole R. Preventing chronic disease: a priority for global health. International Journal of Epidemiology. Apr 2006; 35(2): 492-494.
- Epping-Jordan JE et al. Preparing the 21st century global healthcare workforce. BMJ. 2005 Mar 19; 330(7492): 637-639.
- Sergienko IV, Shestakova MV, Boytsov SA. «Extreme risk category in the system of stratification of cardiovascular complications» Consensus of the Council of Experts «National Society of Atherosclerosis», Non-governmental organization «Russian Association of Endocrinologists», «National League of Cardiogenetics», Russian Society of Cardiology, 2018: 15-26. (In Russ.).
- Oganov RG, Kontsevaya AV, Kalinina AM. Economic damage from cardiovascular diseases in the Russian Federation. Cardiovascular therapy and prevention. 2011; 10 (4): 4-9. (In Russ.).
- Mortality trends in Russia at the beginning of the XXI century (according to official statistics). Cardiovascular therapy and prevention. 2011, 10 (6): 5–10). (In Russ.).
- National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation, 2002. Vol. 106, No. 25, pp. 3143–3421. PMID 12485966.
- Conroy RM, et al. SCORE project group. Estimation of ten-year risk of fatal cardiovascular disease inEurope: the SCORE project. Eur Heart J.: 2003, Vol. 24, No. 11, pp. 987–1003. PMID 12788299.
- Graham I, et al. European guidelines on cardiovascular disease prevention in clinical practice: full text. Fourth Joint Task Force of the European Society of Cardiology and other societies on cardiovascular disease prevention in clinical practice. Eur J Cardiovasc Prev Rehabil: 2007. Vol. 14, No. Suppl 2, pp. S1–113. PMID 17726407.
- ESC/EAS Recommendations for dyslipidemia Treatment: Lipid Modification to Reduce Cardiovascular Risk 2019. Russian Journal of Cardiology. 2020; 25(5): 3826. DOI: 10.15829/1560-4071-2020-3826 (In Russ.).
- Arnett DK, Blumenthal RS, Albert MA, et al. Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary. 2019 ACC/AHA.Journal of the American College of Cardiology.2019, 26028. DOI: 10.1016/j.jacc.2019.03.009.
- Tuomilehto J. Identification of people at high risk for CVD or diabetes. University of Helsinki, Finland, 2020.
- EASD, ESC Recommendations for Diabetes, Prediabetes and COVD: European Society of Cardiology (ESC), European Association for the Study of Diabetes (EASD), 2014. (In Russ.).
- Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohortstudy. Lancet, 2020; 395:1054-1062.
- Sisti N, Valente S, Mandoli GE, et al. COVID-19 in patients with heart failure: the new and the old epidemic. Postgraduate Medical Journal, 2021; 97: 175-179.
- URL: https://www.heart.org/en/news/2020/09/03/what-covid-19-is-doing-to-the-heart-even-after-recovery.
- Kofman A. Introduction to the theory of fuzzy sets. Moscow, Radio i swyaz, 1982. (In Russ.).
- Borisov VV, Kruglov VV, Fedulov AS. Fuzzy models and networks. Moscow, Goryachaya liniya-Telekom, 2012, pp. 182-185. (In Russ.).
- Filippov YuA, Korpan NN, Tyutyunnik VM. Technologies for determining the functional and physiological state of a person. Bulletin of the Russian New University. Ser.: Complex systems: models, analysis, control. 2020;5:63-71. DOI: 10.25586/RNU.V9187.20.05.P.063. (In Russ.).
- Tyutyunnik VM. Thermodynamics of irreversible processes and self-organization in information flows. International Journal of Advanced Research in IT and Engineering. 2019;8(3):18-24.
- Tyutyunnik, VM. The Nobel Congress has completed its work in Tambov. Cardiometry. 2019; November, 15:73-77.
