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Pregled bibliografske jedinice broj: 1199057

Country-level factor influence on COVID-19 excess mortality rates determined using Random Forest algorithm


Baressi Šegota, Sandi; Blagojević, Anđela; Šušteršić, Tijana; Musulin, Jelena; Štifanić, Daniel; Glučina, Matko; Lorencin, Ivan; Anđelić, Nikola; Filipović, Nenad; Car, Zlatan
Country-level factor influence on COVID-19 excess mortality rates determined using Random Forest algorithm // Ri-STEM-2022 Proceedings
Rijeka, Hrvatska, 2022. str. 16-18 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1199057 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Country-level factor influence on COVID-19 excess mortality rates determined using Random Forest algorithm

Autori
Baressi Šegota, Sandi ; Blagojević, Anđela ; Šušteršić, Tijana ; Musulin, Jelena ; Štifanić, Daniel ; Glučina, Matko ; Lorencin, Ivan ; Anđelić, Nikola ; Filipović, Nenad ; Car, Zlatan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Ri-STEM-2022 Proceedings / - , 2022, 16-18

Skup
RI-STEM-2022

Mjesto i datum
Rijeka, Hrvatska, 8-9.6.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
artificial intelligence, COVID-19, coronavirus modelling, feature importance, feature permutation machine learning, mean decrease in impurity, random forest regressor

Sažetak
The reaction to and the effect severity on the population of COVID-19 varied across different countries. This indicates that there are country-level factors which influenced the severity COVID-19 effect on the populace. The goal of this research is to determine some of these factors using Our World in Data COVID-19 dataset. The performance of the country is evaluated using excess mortality (EM), while the inputs selected for the analysis are stringency index (SI), population (P), population density (PD), median age (MA), percent of population aged 65 or older (P65), percent of population aged 70 or older (P70), GDP per capita (GDP), percentage of population living in extreme poverty (EP), death rate from cardiovascular diseases (CDR), prevalence of diabetes withing population (DP), percentage of female smokers (FS), percentage of male smokers (MS), availability of handwashing facilities (HWF), hospital beds available per thousand (HB), life expectancy (LE), and human development index (HDI). The inputs are evaluated using Random Forest (RF) algorithm via two metrics Mean Decrease in Impurity (MDI) and Feature Permutation (FP). Results show that the SI is the most influential factor when evaluated using RF.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Strojarstvo



POVEZANOST RADA


Projekti:
Ostalo-CEI - 305.6019-20 - Use of regressive artificial intelligence (AI) and machine learning (ML) methods in modelling of COVID-19 spread (COVIDAi) (Car, Zlatan, Ostalo - CEI Extraordinary Call for Proposals 2020) ( POIROT)
EK-EFRR-KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša, EK - KK.01.2.2.03) ( POIROT)
EK-KF-KK.01.1.1.01.0009-1 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima - IJ za znanost o podatcima (Lončarić, Sven, EK - KK.01.1.1.01) ( POIROT)

Ustanove:
Tehnički fakultet, Rijeka

Poveznice na cjeloviti tekst rada:

sites.google.com

Citiraj ovu publikaciju:

Baressi Šegota, Sandi; Blagojević, Anđela; Šušteršić, Tijana; Musulin, Jelena; Štifanić, Daniel; Glučina, Matko; Lorencin, Ivan; Anđelić, Nikola; Filipović, Nenad; Car, Zlatan
Country-level factor influence on COVID-19 excess mortality rates determined using Random Forest algorithm // Ri-STEM-2022 Proceedings
Rijeka, Hrvatska, 2022. str. 16-18 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Baressi Šegota, S., Blagojević, A., Šušteršić, T., Musulin, J., Štifanić, D., Glučina, M., Lorencin, I., Anđelić, N., Filipović, N. & Car, Z. (2022) Country-level factor influence on COVID-19 excess mortality rates determined using Random Forest algorithm. U: Ri-STEM-2022 Proceedings.
@article{article, author = {Baressi \v{S}egota, Sandi and Blagojevi\'{c}, An\djela and \v{S}u\v{s}ter\v{s}i\'{c}, Tijana and Musulin, Jelena and \v{S}tifani\'{c}, Daniel and Glu\v{c}ina, Matko and Lorencin, Ivan and An\djeli\'{c}, Nikola and Filipovi\'{c}, Nenad and Car, Zlatan}, year = {2022}, pages = {16-18}, keywords = {artificial intelligence, COVID-19, coronavirus modelling, feature importance, feature permutation machine learning, mean decrease in impurity, random forest regressor}, title = {Country-level factor influence on COVID-19 excess mortality rates determined using Random Forest algorithm}, keyword = {artificial intelligence, COVID-19, coronavirus modelling, feature importance, feature permutation machine learning, mean decrease in impurity, random forest regressor}, publisherplace = {Rijeka, Hrvatska} }
@article{article, author = {Baressi \v{S}egota, Sandi and Blagojevi\'{c}, An\djela and \v{S}u\v{s}ter\v{s}i\'{c}, Tijana and Musulin, Jelena and \v{S}tifani\'{c}, Daniel and Glu\v{c}ina, Matko and Lorencin, Ivan and An\djeli\'{c}, Nikola and Filipovi\'{c}, Nenad and Car, Zlatan}, year = {2022}, pages = {16-18}, keywords = {artificial intelligence, COVID-19, coronavirus modelling, feature importance, feature permutation machine learning, mean decrease in impurity, random forest regressor}, title = {Country-level factor influence on COVID-19 excess mortality rates determined using Random Forest algorithm}, keyword = {artificial intelligence, COVID-19, coronavirus modelling, feature importance, feature permutation machine learning, mean decrease in impurity, random forest regressor}, publisherplace = {Rijeka, Hrvatska} }




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