The Hierarchical Classifier for COVID-19 Resistance Evaluation

Nataliya Shakhovska, Ivan Izonin and Nataliia Melnykova
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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

Data, 2021, vol. 6, issue 1, 1-17

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.

Keywords: classification; clustering; COVID-19; data analysis; data visualization; feature selection (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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