RESEARCH OF MODELS AND ALGORITHMS OF REMOTE MONITORING OF ARTERIAL PRESSURE

Ismailov O.M.; Mirzakhalilov S.S.

doi:10.5281/zenodo.8197523

RESEARCH OF MODELS AND ALGORITHMS OF REMOTE MONITORING OF ARTERIAL PRESSURE

30.07.2023 International Scientific Journal "Science and Innovation". Series A. Volume 2 Issue 7

Ismailov O.M., Mirzakhalilov S.S.

Abstract. Monitoring blood pressure is an important aspect of human health. Modern technologies for monitoring blood pressure allow for increased accuracy of measurements and facilitate the process of controlling this health indicator. The use of artificial intelligence and machine learning can help develop even more accurate and convenient monitoring methods, which will be beneficial for many people around the world. This article explores the tasks of developing a predictive algorithm for identifying individuals at high risk of developing hypertension without the need for invasive clinical procedures.

Keywords: blood pressure monitoring, artificial intelligence, hypertension, neural network, blood pressure monitoring algorithm, model.

References:

1. Singh S, Shankar R, Singh GP. Prevalence and associated risk factors of hypertension: A cross-sectional study in urban Varanasi. Int J Hypertens. 2017:5491838. pmid:29348933. 2. Hypertension. URL:https://www.who.int/ru/news-room/fact-sheets/detail /hypertension. 2023 March 6. 3. Bolivar JJ. Essential hypertension: an approach to its etiology and neurogenic pathophysiology. Int J Hypertens. 2013;2013:547809. pmid:24386559. 4. Rapport RS. Hypertension. Silent killer. N J Med. 1999;96(3):41–3. pmid:15038235. 5. Bouchra Bakr Mouhtadi , Reem Mohamad Najib Kanaan , Mohammad Iskandarani , Mohamad Khaled Rahal , Dalal Hammoudi Halat. Prevalence, awareness, treatment, control and risk factors associated with hypertension in Lebanese adults: A cross sectional study. National Library of Medicine. 2018;2018(1):6. pmid: 29644233. 6. Alemayehu Zekewos, Tariku Egeno, Eskindir Loha. The magnitude of hypertension and its risk factors in southern Ethiopia: A community based study. 2019 Aug 28;14(8):e0221726. doi: 10.1371/journal.pone.0221726. pmid:31461475. 7. Allah Rakha, Nehal Umar, Roshina Rabail, Masood Sadiq Butt, Marek Kieliszek, Abdo Hassoun, Rana Muhammad Aadil. Anti-inflammatory and anti-allergic potential of dietary flavonoids: A review. 2022 Dec;156:113945. doi: 10.1016/j.biopha.2022.113945. PMID: 36411631. 8. Vasant Hirani, Paola Zaninotto, Paola Primatesta. Generalised and abdominal obesity and risk of diabetes, hypertension and hypertension-diabetes co-morbidity in England. 2008 May;11(5):521-7. doi: 10.1017/S1368980007000845. PMID: 17767799. 9. Jacqueline R. Halladay , Katrina E. Donahue, Crystal W. Cené , Quefeng Li, Doyle M. Cummings, Alan L. Hinderliter, Cassandra L. Miller, Beverly A. Garcia, Edwin Little, Margorie Rachide, Jim Tillman, Alice S. Ammerman, Darren DeWalt. The association of health literacy and blood pressure reduction in a cohort of patients with hypertension: The heart healthy lenoir trial. 2017 Mar;100(3):542-549. doi: 10.1016/j.pec.2016.10.015. Epub 2016 Oct 20. PMID: 27776790. 10. Amit J. Shah, Emir Veledar, Yuling Hong, J. Douglas Bremner, Viola Vaccarino. Depression and history of attempted suicide as risk factors for heart disease mortality in young individuals. 2011 Nov;68(11):1135-42. doi: 10.1001/archgenpsychiatry.2011.125. PMID: 22065529. 11. Juhwan Noh, Hyeon Chang Kim, Anna Shin, Hyungseon Yeom, Suk-Yong Jang, Jung Hyun Lee, Changsoo Kim, Il Suh. Prevalence of Comorbidity among People with Hypertension: The Korea National Health and Nutrition Examination Survey 2007-2013. 2016 Sep;46(5):672-680. doi: 10.4070/kcj.2016.46.5.672. Epub 2016 Sep 28. PMID: 27721859. 12. Priyanga Ranasinghe, Dilini N Cooray, Ranil Jayawardena, Prasad Katulanda. The influence of family history of hypertension on disease prevalence and associated metabolic risk factors among Sri Lankan adults. 2015 Jun 20;15:576. doi: 10.1186/s12889-015-1927-7. PMID: 26092387 13. Nicola Mucci, Gabriele Giorgi, Stefano De Pasquale Ceratti, Javier Fiz-Pérez, Federico Mucci, Giulio Arcangeli. Anxiety, Stress-Related Factors, and Blood Pressure in Young Adults. 2016 Oct 28;7:1682. doi: 10.3389/fpsyg.2016.01682. PMID: 27840615. 14. Aruni Bhatnagar, Wasim Maziak, Thomas Eissenberg, Kenneth D. Ward, George Thurston, Brian A. King, Erin L. Sutfin, Caroline O. Cobb, Merlyn Griffiths, Larry B. Goldstein, Mary Rezk-Hanna. Water Pipe (Hookah) Smoking and Cardiovascular Disease Risk: A Scientific Statement From the American Heart Association. 2019 May 7;139(19):e917-e936. doi: 10.1161/CIR.0000000000000671. PMID: 30845826. 15. Sheila M. Hegde, Scott D. Solomon. Influence of Physical Activity on Hypertension and Cardiac Structure and Function. National Library of Medicine. Curr Hypertens Rep. 2015;17(10):77. PMID: 26277725. 16. Echouffo-Tcheugui JB, Batty GD, Kivimäki M, Kengne AP. Risk models to predict hypertension: a systematic review. PLoS One. 2013;8(7). 17. Kivimäki M., Batty G.D., Singh-Manoux A., Ferrie J.E., Tabak A.G., Jokela M., et al. Validating the Framingham hypertension risk score: results from the Whitehall II Study. Hypertension. 2009;54(3):496–501. PMID:19597041. 18. LaFreniere D., Zulkernine F., Barber D., Martin K., editors. Using machine learning to predict hypertension from a clinical dataset. 2016 IEEE Symposium Series on Computational Intelligence (SSCI); 2016: IEEE. 19. Krittanawong C., Bomback A.S., Baber U., Bangalore S., Messerli F.H., Tang W.W. Future direction for using artificial intelligence to predict and manage hypertension. Curr Hypertens Rep. 2018;20(9):75. PMID:29980865. 20. Echouffo-Tcheugui J.B., Batty G.D., Kivimäki M., Kengne A.P. Risk models to predict hypertension: a systematic review. PLoS One. 2013;8(7):e67370. PMID:23861760. 21. Ramezankhani A, Kabir A, Pournik O, Azizi F, Hadaegh FJM. Classification-based data mining for identification of risk patterns associated with hypertension in Middle Eastern population: A 12-year longitudinal study. 2016;95(35). 22. Alvarez Aliaga A., Gonzalez-Aguilera J.C., Maceo-Gomez L.D.R., Suarez-Quesada A. Predictive model for the development of hypertensive cardiopathy: A prospective cohort study. Medwave. 2017;17(4):e6954. PMID:28582382. 23. Sun J., McNaughton CD., Zhang P., Perer A., Gkoulalas-Divanis A., Denny J.C., et al. Predicting changes in hypertension control using electronic health records from a chronic disease management program. J Am Med Inform Assoc. 2014;21(2):337–44. PMID:24045907. 24. Huang S., Xu Y., Yue L., Wei S., Liu L., Gan X., et al. Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area. Hypertens Res. 2010;33(7):722–6. PMID:20505678. 25. Ismailov O.M., Mirzakhalilov S., Ismoilov M.O. Research of methods and algorithms of replacation in systems with a distributed database. Problems of Computational and Applied Mathematics. 2023. 1(46): 116-122. 26. Ismailov О.М., Mirzakhalilov С. "Survey of algorithms search of shortest ways for design of geolocation information systems." Science and Innovation 2.3 (2023): 62-70. https://doi.org/10.5281/zenodo.7729386. 27. Ismailov О.М., Mirzakhalilov S.S., Kholdarova G.N. “Methods of using artificial intelligence algorithms in monitoring the health of athletes during training and competition”. International scientific and technical conference “Digital technologies: Problems and solutions of practical implementation in the spheres”. April 27-28, 2023.