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A Comparative Analysis on Improving Covid-19 Prediction by Using Ensemble Learning Methods

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Digitizing Production Systems

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

In this study, it is aimed to improve the Covid-19 predictions in terms of the distinction between Covid-19 and Flu by using several well-known ensemble learning methods namely, majority voting, bagging, boosting, and stacking. In this scope, the performance of base machine learning models was compared with the ensemble ones (majority voting, C5.0, stochastic gradient boosting, bagged CART, random forest, and stacking models) on a public Covid-19 dataset in which observations are labelled as Covid-19 and Flu. Since the task belongs to a classification problem, supervised machine learning algorithms (logistic regression (via generalized linear model), classification and regression trees, artificial neural networks, and support vector machines) are used as base learners. The Cross-Industry Standard Process Model for Data Mining, which is consisted of six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment, is used as the study method. In the model performance evaluation stage, an additional metric was proposed by considering the accuracy and its change interval (max-min). The performance of the models was discussed in terms of accuracy and the proposed metric. A Shiny application is developed by using the best performing model. The application enables users to predict Covid-19 status through a web interface and to use it interactively. Analyses are performed with R and RStudio.

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Acknowledgement

The author would like to thank to Prof. Dr. M. Erdal BALABAN for his valuable comments and suggestions.

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Authors and Affiliations

  1. Istanbul University, Istanbul, Turkey

    Elif Kartal

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  1. Elif Kartal
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Correspondence to Elif Kartal .

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Editors and Affiliations

  1. Faculty of Mechanical and Industrial Engineering, Institute of Production Engineering and Photonic Technologies, TU Wien (Vienna University of Technology), Vienna, Austria

    Numan M. Durakbasa

  2. Faculty of Engineering, Industrial Engineering Department, İstanbul Aydın University, İstanbul, Turkey

    M. Güneş Gençyılmaz

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Kartal, E. (2022). A Comparative Analysis on Improving Covid-19 Prediction by Using Ensemble Learning Methods. In: Durakbasa, N.M., Gençyılmaz, M.G. (eds) Digitizing Production Systems. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-90421-0_1

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  • DOI: https://doi.org/10.1007/978-3-030-90421-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90420-3

  • Online ISBN: 978-3-030-90421-0

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