bebi 21: e2

Research Article

Automated Pipeline for Continual Data Gathering and Retraining of the Machine Learning-Based COVID-19 Spread Models

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  • @ARTICLE{10.4108/eai.4-5-2021.169582,
    author={S. Baressi Šegota and I. Lorencin and N. Anđelić and D. Štifanić and J. Musulin and S. Vlahinić and T. Šušteršič and A. Blagojević and Z. Car},
    title={Automated Pipeline for Continual Data Gathering and Retraining of the Machine Learning-Based COVID-19 Spread Models},
    journal={EAI Endorsed Transactions on Bioengineering and Bioinformatics: Online First},
    volume={},
    number={},
    publisher={EAI},
    journal_a={BEBI},
    year={2021},
    month={5},
    keywords={Artificial Intelligence, Bio-engineering, Bio-inspired systems, Bio-inspired models, COVID-19, Epidemiology Curves, Machine Learning, Multilayer Perceptron},
    doi={10.4108/eai.4-5-2021.169582}
}
  • S. Baressi Šegota
    I. Lorencin
    N. Anđelić
    D. Štifanić
    J. Musulin
    S. Vlahinić
    T. Šušteršič
    A. Blagojević
    Z. Car
    Year: 2021
    Automated Pipeline for Continual Data Gathering and Retraining of the Machine Learning-Based COVID-19 Spread Models
    BEBI
    EAI
    DOI: 10.4108/eai.4-5-2021.169582
S. Baressi Šegota1,*, I. Lorencin1, N. Anđelić1, D. Štifanić1, J. Musulin1, S. Vlahinić1, T. Šušteršič2,3, A. Blagojević2,3, Z. Car1
  • 1: University of Rijeka, Faculty of Engineering, Vukovarska 58, 51000 Rijeka, Croatia
  • 2: Bioengineering Research and Development Center (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia
  • 3: University of Kragujevac, Faculty of Engineering, Sestre Janjić 6, 34000 Kragujevac, Serbia
*Contact email: sbaressisegota@riteh.hr

Abstract

INTRODUCTION: The development of epidemiological curve models is one of the key factors in the combat of epidemiological diseases such as COVID-19.

OBJECTIVES: The goal of this paper is to develop a system for automatic training and testing of AI-based regressive models of epidemiological curves using public data, which involves automating the data acquisition and speeding up the training of the models.

METHODS: The research applies Multilayer Perceptron (MLP) for the creation of models, implemented within a system for automatic data fetching and training, and e valuated using the coefficient of determination (R2). Training time is lowered through the application of data filtering and simplifying the model selection.

RESULTS: The developed system can train high precision models rapidly, allowing for quick model delivery All trained models achieve scores which are higher than 0.95.

CONCLUSION: The results show that the development of a quick COVID-19 spread modeling system is possible.

Keywords
Artificial Intelligence, Bio-engineering, Bio-inspired systems, Bio-inspired models, COVID-19, Epidemiology Curves, Machine Learning, Multilayer Perceptron
Received
2021-03-13
Accepted
2021-05-03
Published
2021-05-04
Publisher
EAI
http://dx.doi.org/10.4108/eai.4-5-2021.169582

Copyright © 2021 S. Baressi Šegota et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited