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The Hierarchical Classifier for COVID-19 Resistance Evaluation

Author

Listed:
  • Nataliya Shakhovska

    (Department of Artificial Intelligence, Lviv Polytechnic National University, 5 Kniazia Romana str., 79000 Lviv, Ukraine)

  • Ivan Izonin

    (Department of Artificial Intelligence, Lviv Polytechnic National University, 5 Kniazia Romana str., 79000 Lviv, Ukraine)

  • Nataliia Melnykova

    (Department of Artificial Intelligence, Lviv Polytechnic National University, 5 Kniazia Romana str., 79000 Lviv, Ukraine)

Abstract

Finding dependencies in the data requires the analysis of relations between dozens of parameters of the studied process and hundreds of possible sources of influence on this process. Dependencies are nondeterministic and therefore modeling requires the use of statistical methods for analyzing random processes. Part of the information is often hidden from observation or not monitored. That is why many difficulties have arisen in the process of analyzing the collected information. The paper aims to find frequent patterns and parameters affected by COVID-19. The novelty of the paper is hierarchical architecture comprises supervised and unsupervised methods. It allows the development of an ensemble of the methods based on k-means clustering and classification. The best classifiers from the ensemble are random forest with 500 trees and XGBoost. Classification for separated clusters gives us higher accuracy on 4% in comparison with dataset analysis. The proposed approach can be used also for personalized medicine decision support in other domains. The features selection allows us to analyze the following features with the highest impact on COVID-19: age, sex, blood group, had influenza.

Suggested Citation

  • Nataliya Shakhovska & Ivan Izonin & Nataliia Melnykova, 2021. "The Hierarchical Classifier for COVID-19 Resistance Evaluation," Data, MDPI, vol. 6(1), pages 1-17, January.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:1:p:6-:d:481196
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    References listed on IDEAS

    as
    1. Tatiana Petukhova & Davor Ojkic & Beverly McEwen & Rob Deardon & Zvonimir Poljak, 2018. "Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-17, June.
    2. Rendani Mbuvha & Tshilidzi Marwala, 2020. "Bayesian inference of COVID-19 spreading rates in South Africa," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-16, August.
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