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Search for Hidden Patterns in the Study of Coronavirus Patients Using Data Mining Methods

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12th World Conference “Intelligent System for Industrial Automation” (WCIS-2022) (WCIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 718))

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Abstract

At the present time, when predicting the spread of the COVID-19 virus, the condition of patients with COVID-19 and the development of an antiviral vaccine, the method of artificial data analysis is actively used. In modern medicine, to improve the quality of medical care, increase the accuracy of diagnostics and reduce potential errors, methods based on artificial intelligence and machine learning are actively used. In this paper the reasons that affect the accuracy of the definition of coronavirus have been investigated. The set of patients is divided into five non-overlapping subsets (classes) according to the severity of the disease. To detect hidden patterns in the analysis, non-linear data transformations are used based on the calculation of the values of the class membership function for each feature, also used the formation of latent features based on generalized estimates. The values of thresholds between classes on the numerical axis are determined, both for individual features and generalized estimates of objects on the defined sets of features.

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References

  1. Chen, N., Zhou, M., Dong, X., et al.: Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395(10223), 507–513 (2020). https://doi.org/10.1016/S0140-6736(20)30211-7

    Article  Google Scholar 

  2. WHO Director-General’s remarks at the media briefing on 2019-nCoV on 11 February 2020 (2020). https://www.who.int/director-general/speeches/detail/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020

  3. Huang, C., Wang, Y., Li, X., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan. China. Lancet 395(10223), 497–506 (2020). https://doi.org/10.1016/S0140-6736(20)30183-5

    Article  Google Scholar 

  4. Teich, V.D., Klajner, S., Almeida, F.A.S., et al.: Epidemiologic and clinical features of patients with COVID-19 in Brazil. Einstein (São Paulo) (2020). https://doi.org/10.31744/einstein_journal/2020AO6022

    Article  Google Scholar 

  5. Immovilli, P., Morelli, N., Antonucci, E., et al.: COVID-19 mortality and ICU admission: the Italian experience. Crit. Care 24(1), 228 (2020). https://doi.org/10.1186/s13054-020-02957-9

    Article  Google Scholar 

  6. Jain, V., Yuan, J.M.: Predictive symptoms and comorbidities for severe COVID-19 and intensive care unit admission: a systematic review and meta-analysis. Int. J. Public Health 65(5), 533–546 (2020). https://doi.org/10.1007/s00038-020-01390-7

    Article  Google Scholar 

  7. Alimadadi, A.A., Aryal, S., Manandhar, I., Munroe, P.B., Joe, B., Cheng, X.: Artificial intelligence and machine learning to fight COVID-19. Physiol. Genomics 52, 200–202 (2020)

    Google Scholar 

  8. Khadjibayev, A.M., Adilova, F.T., Kasimov, K., Sharipova, V., Iskhakov, N.B.: The role of artificial intelligence in predicting Covid-19 problems: an analytical review. Bull. Emergency Med. 13(4), 75–85 (2020)

    Google Scholar 

  9. Madrakhimov, S., Rozikhodjaeva, G.A., Makharov, K.T.: Construction of fuzzy inference rules for medicine diagnostics problems. J. Phys. Conf. Ser. 2032, 012032 (2021). https://doi.org/10.1088/1742-6596/2032/1/012032

    Article  Google Scholar 

  10. Ignatev, N.A.: Computing generalized parameters and data mining. Autom. Remote. Control. 72, 1068–1074 (2011). https://doi.org/10.1134/S0005117911050146

    Article  MathSciNet  Google Scholar 

  11. Ignatyev, N.A., Rakhimova, M.A.: Formation and analysis of sets of informative features of objects by pairs of classes. Artif. Intell. Decis. Making 4, 18–26 (2021). https://doi.org/10.14357/20718594210402

    Article  Google Scholar 

  12. Ignatyev, N.: On nonlinear transformations of features based on the functions of objects belonging to classes. Pattern Recognit. Image Anal. 31, 197–204 (2021). https://doi.org/10.1134/S1054661821020085

    Article  Google Scholar 

  13. Madrakhimov, S., Makharov, K.T., Khurramov, A.: Selection of informative features with considering data gaps. Probl. Comput. Appl. Math. 4(42), 147–157 (2022)

    Google Scholar 

  14. Broa, R., Smilde, A.K.: Principal component analysis. Anal. Methods 6, 2812–2831 (2014)

    Article  Google Scholar 

  15. Maaten, L., Hinton, G.: Vizualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    Google Scholar 

  16. Rocha, A., Goldenstein, S.: Multiclass from binary: expanding one-versus-all, one-versus-one and ECOC-based approaches. IEEE Trans. Neural Networks Learn. Syst. 25, 289–302 (2014). https://doi.org/10.1109/TNNLS.2013.2274735

    Article  Google Scholar 

  17. Rakhimova, M.A.: On the quantitative estimation of the state of health by age groups, III International Book Edition of the Countries of Independent States, “The Best Young Scientist-2021”, pp. 73–77 (2021)

    Google Scholar 

  18. Ignatev, N., Lolaev, M.: Decision-making within the framework of an object’s own features space. Probl. Comput. Appl. Math. 4(13), 5–13 (2018)

    Google Scholar 

  19. Ignatyev, N., Mirzaev, A.: Selection of features into the object’s own space based on the measure of its compactness. Tomsk State Univ. J. Control Comput. Sci. 49, 55–62 (2019). https://doi.org/10.17223/19988605/49/7

    Article  Google Scholar 

  20. Saidov, D.: Data visualization and its proof by compactness criterion of objects of classes. Int. J. Intell. Syst. Appl. (IJISA) 9, 51–58 (2017). https://doi.org/10.5815/ijisa.2017.08.06

    Article  Google Scholar 

  21. Kim, K., et al.: Clinical features of COVID-19 in Uzbekistan. J. Korean Med. Sci. 35(45), e404 (2020). https://doi.org/10.3346/jkms.2020.35.e404

    Article  Google Scholar 

  22. Interim guidelines for the management of patients infected with COVID-19, 6th edn. The Ministry of Health of the Republic of Uzbekistan (2020)

    Google Scholar 

  23. Responding to the COVID-19 pandemic: WHO’s action in countries, territories and areas, 2020. World Health Organization (2021)

    Google Scholar 

  24. Harris, C.R., Millman, K.J., et al.: Array programming with NumPy. Nature 585(7825), 357–362 (2020). https://doi.org/10.1038/s41586-020-2649-2

    Article  Google Scholar 

  25. Pedregosa, F., Varoquaux, G., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Korea University, Seoul, South Korea

    Bakhodir Rakhimov

  2. Tashkent Medical Academy, Tashkent, Uzbekistan

    Bakhodir Rakhimov

  3. Tashkent State Transport University, Tashkent, Uzbekistan

    Alimov Akrom

  4. National University of Uzbekistan, Tashkent, Uzbekistan

    Rakhimova Mekhrbonu & Makharov Kodirbek

  5. Yeoju Technical Institute in Tashkent, Tashkent, Uzbekistan

    Bakhodir Rakhimov, Rakhimova Mekhrbonu & Makharov Kodirbek

Authors
  1. Bakhodir Rakhimov
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  2. Alimov Akrom
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  3. Rakhimova Mekhrbonu
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  4. Makharov Kodirbek
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Corresponding author

Correspondence to Bakhodir Rakhimov .

Editor information

Editors and Affiliations

  1. Joint MBA Program, Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    R. A. Aliev

  2. Department Automation of Production Processes, Tashkent State Technical University, Tashkent, Uzbekistan

    Nodirbek Rustambekovich Yusupbekov

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    M. B. Babanli

  6. Department of Computer Engineering, Near East University, Nicosia, Türkiye

    Fahreddin M. Sadikoglu

  7. Tashkent State Technical University, Tashkent, Uzbekistan

    S. M. Turabdjanov

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Rakhimov, B., Akrom, A., Mekhrbonu, R., Kodirbek, M. (2024). Search for Hidden Patterns in the Study of Coronavirus Patients Using Data Mining Methods. In: Aliev, R.A., et al. 12th World Conference “Intelligent System for Industrial Automation” (WCIS-2022). WCIS 2022. Lecture Notes in Networks and Systems, vol 718. Springer, Cham. https://doi.org/10.1007/978-3-031-51521-7_41

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