Applying Regressive Machine Learning Techniques in Determination of COVID-19 Vaccinated Patients’ Influence on the Number of Confirmed and Deceased Patients

Baressi Šegota, Sandi; Lorencin, Ivan; Anđelić, Nikola; Musulin, Jelena; Štifanić, Daniel; Glučina, Matko; Vlahinić, Saša; Car, Zlatan

doi:10.3390/math10162925

Pregled bibliografske jedinice broj: 1210157

Applying Regressive Machine Learning Techniques in Determination of COVID-19 Vaccinated Patients’ Influence on the Number of Confirmed and Deceased Patients

Baressi Šegota, Sandi; Lorencin, Ivan; Anđelić, Nikola; Musulin, Jelena; Štifanić, Daniel; Glučina, Matko; Vlahinić, Saša; Car, Zlatan

Applying Regressive Machine Learning Techniques in Determination of COVID-19 Vaccinated Patients’ Influence on the Number of Confirmed and Deceased Patients // Mathematics, 10 (2022), 16; 1-24 doi:10.3390/math10162925 (međunarodna recenzija, članak, znanstveni)

CROSBI ID: 1210157 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Applying Regressive Machine Learning Techniques in Determination of COVID-19 Vaccinated Patients’ Influence on the Number of Confirmed and Deceased Patients

Autori
Baressi Šegota, Sandi ; Lorencin, Ivan ; Anđelić, Nikola ; Musulin, Jelena ; Štifanić, Daniel ; Glučina, Matko ; Vlahinić, Saša ; Car, Zlatan

Izvornik
Mathematics (2227-7390) 10 (2022), 16; 1-24

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
COVID-19 ; cross-correlation analysis ; machine learning ; regression modeling ; vaccination rates

Sažetak
Vaccinations are one of the most important steps in combat against viral diseases such as COVID-19. Determining the influence of the number of vaccinated patients on the infected population represents a complex problem. For this reason, the aim of this research is to model the influence of the total number of vaccinated or fully vaccinated patients on the number of infected and deceased patients. Five separate modeling algorithms are used: Linear Regression (LR), Logistic Regression (LogR), Least Absolute Shrinkage and Selection Operator (LASSO), Multilayer Perceptron (MLP), and Support Vector Regression (SVR). Cross-correlation analysis is performed to determine the optimal lags in data to assist in obtaining better scores. The cross-validation of models is performed, and the models are evaluated using Mean Absolute Percentage Error (MAPE). The modeling is performed for four different countries: Germany, India, the United Kingdom (UK), and the United States of America (USA). Models with an error below 1% are found for all the modeled cases, with the best models being achieved either by LR or MLP methods. The obtained results indicate that the influence of vaccination rates on the number of confirmed and deceased patients exists and can be modeled using ML methods with relatively high precision.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Računarstvo, Temeljne tehničke znanosti, Temeljne medicinske znanosti, Javno zdravstvo i zdravstvena zaštita

POVEZANOST RADA

Projekti:
--KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša) ( CroRIS)

Ustanove:
Tehnički fakultet, Rijeka,
Sveučilište u Rijeci

Profili:

Nikola Anđelić (autor)