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

Šegota, S. Baressi and Lorencin, I. and Anđelić, N. and Štifanić, D. and Musulin, J. and Vlahinić, S. and Šušteršič, T. and Blagojević, A. and Car, Z. (2021) Automated Pipeline for Continual Data Gathering and Retraining of the Machine Learning-Based COVID-19 Spread Models. EAI Endorsed Transactions on Bioengineering and Bioinformatics, 1 (21). e2. ISSN 2709-4111

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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

Item Type: Article
Uncontrolled Keywords: Artificial Intelligence, Bio-engineering, Bio-inspired systems, Bio-inspired models, COVID-19, Epidemiology Curves, Machine Learning, Multilayer Perceptron
Subjects: Q Science > Q Science (General)
Depositing User: EAI Editor IV
Date Deposited: 09 Jul 2021 08:26
Last Modified: 09 Jul 2021 08:26
URI: https://eprints.eudl.eu/id/eprint/4282

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