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COVID-19 Predictive Models Based on Grammatical Evolution.

Ioannis G Tsoulos1, Chrysostomos Stylios1,2, Vlasis Charalampous1

  • 1Department of Informatics and Telecommunications, University of Ioannina, Arta, Greece.

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|February 7, 2023
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Summary
This summary is machine-generated.

This study introduces a novel feature construction method to predict COVID-19 mortality rates. The approach enhances data with artificial features, outperforming other machine learning methods for pandemic prediction.

Keywords:
COVID-19Feature constructionGrammatical evolutionMachine learningPredictive models

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Area of Science:

  • Epidemiology
  • Data Science
  • Machine Learning

Background:

  • The COVID-19 pandemic necessitates accurate mortality rate prediction.
  • Existing machine learning models may benefit from improved feature engineering.

Purpose of the Study:

  • To develop and evaluate a novel feature construction method for predicting COVID-19 monthly mortality rates.
  • To demonstrate the efficacy of grammatically guided feature construction.

Main Methods:

  • A feature construction method using a grammatical guided procedure was developed.
  • Artificial features were generated and integrated into original datasets.
  • Machine learning models, including artificial neural networks, were applied to modified datasets.

Main Results:

  • Comparative experiments showed the advantage of feature construction.
  • The proposed method demonstrated superior performance in predicting pandemic elements.

Conclusions:

  • Feature construction is a valuable technique for enhancing pandemic-related predictions.
  • The grammatically guided approach offers a promising direction for future epidemiological modeling.