COVID-19 Predictive Models Based on Grammatical Evolution

Ioannis G Tsoulos; Chrysostomos Stylios; Vlasis Charalampous

doi:10.1007/s42979-022-01632-w

COVID-19 Predictive Models Based on Grammatical Evolution

SN Comput Sci. 2023;4(2):191. doi: 10.1007/s42979-022-01632-w. Epub 2023 Feb 2.

Authors

Ioannis G Tsoulos¹, Chrysostomos Stylios^{1

2}, Vlasis Charalampous¹

Affiliations

¹ Department of Informatics and Telecommunications, University of Ioannina, Arta, Greece.
² Industrial Systems Institute, Athena Research Center, Patras Science Park Building, Platani, Patras, Greece.

Abstract

A feature construction method that incorporates a grammatical guided procedure is presented here to predict the monthly mortality rate of the COVID-19 pandemic. Three distinct use cases were obtained from publicly available data and three corresponding datasets were created for that purpose. The proposed method is based on constructing artificial features from the original ones. After the artificial features are generated, the original data set is modified based on these features and a machine learning model, such as an artificial neural network, is applied to the modified data. From the comparative experiments done, it was clear that feature construction has an advantage over other machine learning methods for predicting pandemic elements.

Keywords: COVID-19; Feature construction; Grammatical evolution; Machine learning; Predictive models.