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Pregled bibliografske jedinice broj: 1195802

Analysis of COVID-19 disease using machine learning - personalized model


Blagojević, Anđela; Šušteršič, Tijana; Lorencin, Ivan; Baressi Šegota, Sandi; Anđelić, Nikola; Milovanović, Dragan; Baskić, Dejan; Car, Zlatan; Filipović, Nenad
Analysis of COVID-19 disease using machine learning - personalized model // 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI) : Book of abstracts / Filipović, Nenad (ur.).
Kragujevac: University of Kragujevac, 2022. str. 1-4 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1195802 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Analysis of COVID-19 disease using machine learning - personalized model

Autori
Blagojević, Anđela ; Šušteršič, Tijana ; Lorencin, Ivan ; Baressi Šegota, Sandi ; Anđelić, Nikola ; Milovanović, Dragan ; Baskić, Dejan ; Car, Zlatan ; Filipović, Nenad

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

Izvornik
1st Serbian International Conference on Applied Artificial Intelligence (SICAAI) : Book of abstracts / Filipović, Nenad - Kragujevac : University of Kragujevac, 2022, 1-4

ISBN
978-86-81037-71-3

Skup
1st Serbian International Conference on Applied Artificial Intelligence (SICAAI)

Mjesto i datum
Kragujevac, Srbija, May 19-20, 2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
COVID-19 ; personalized model ; clinical condition assessment ; ensemble model ; rule-based machine learning

Sažetak
Coronavirus disease (COVID-19), since its appearance, has put a large burden on the global health system which have strived to mitigate the pandemic, but mortality of COVID-19 continues to increase. Many authors have employed machine learning (ML) algorithms in the investigation of COVID-19 in order to identify infected individuals, predict their condition in time, predict the outbreaks and forecast certain numbers. Although there are many studies that examine the application of ML in the diagnosis of prognostic biomarkers and survival prediction several days in advance, there is a limited literature dealing with evidence to label patients in more categories (mild, moderate, severe, etc.) that would help not only to respond in a timely manner to prevent lethal results, but also to minimize the number of patients in hospitals where this is not the case. In this paper we present a methodology for classification of patients into 3 distinct classes of clinical condition (mild, moderate and severe) of COVID-19 disease and prediction of the outcome (change of severity of clinical condition) in advance. The results show that XGBoost classifier achieved average accuracy of 88%. The main advantage of our system is that it is a rule-based algorithm which is easier to implement in a real clinical practice, instead of the use of black box models, which are not appealing for real clinical use.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



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)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-275-1447 - Razvoj inteligentnog ekspertnog sustava za online diagnostiku raka mokračnog mjehura (Car, Zlatan, NadSve - UNIRI potpore) ( POIROT)
EK-KF-KK.01.1.1.01.009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) (Lončarić, Sven; Petrović, Ivan; Šmuc, Tomislav; Jokić, Andrej, EK - KK.01.1.1.01) ( POIROT)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada

Citiraj ovu publikaciju:

Blagojević, Anđela; Šušteršič, Tijana; Lorencin, Ivan; Baressi Šegota, Sandi; Anđelić, Nikola; Milovanović, Dragan; Baskić, Dejan; Car, Zlatan; Filipović, Nenad
Analysis of COVID-19 disease using machine learning - personalized model // 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI) : Book of abstracts / Filipović, Nenad (ur.).
Kragujevac: University of Kragujevac, 2022. str. 1-4 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Blagojević, A., Šušteršič, T., Lorencin, I., Baressi Šegota, S., Anđelić, N., Milovanović, D., Baskić, D., Car, Z. & Filipović, N. (2022) Analysis of COVID-19 disease using machine learning - personalized model. U: Filipović, N. (ur.)1st Serbian International Conference on Applied Artificial Intelligence (SICAAI) : Book of abstracts.
@article{article, author = {Blagojevi\'{c}, An\djela and \v{S}u\v{s}ter\v{s}i\v{c}, Tijana and Lorencin, Ivan and Baressi \v{S}egota, Sandi and An\djeli\'{c}, Nikola and Milovanovi\'{c}, Dragan and Baski\'{c}, Dejan and Car, Zlatan and Filipovi\'{c}, Nenad}, editor = {Filipovi\'{c}, N.}, year = {2022}, pages = {1-4}, keywords = {COVID-19, personalized model, clinical condition assessment, ensemble model, rule-based machine learning}, isbn = {978-86-81037-71-3}, title = {Analysis of COVID-19 disease using machine learning - personalized model}, keyword = {COVID-19, personalized model, clinical condition assessment, ensemble model, rule-based machine learning}, publisher = {University of Kragujevac}, publisherplace = {Kragujevac, Srbija} }
@article{article, author = {Blagojevi\'{c}, An\djela and \v{S}u\v{s}ter\v{s}i\v{c}, Tijana and Lorencin, Ivan and Baressi \v{S}egota, Sandi and An\djeli\'{c}, Nikola and Milovanovi\'{c}, Dragan and Baski\'{c}, Dejan and Car, Zlatan and Filipovi\'{c}, Nenad}, editor = {Filipovi\'{c}, N.}, year = {2022}, pages = {1-4}, keywords = {COVID-19, personalized model, clinical condition assessment, ensemble model, rule-based machine learning}, isbn = {978-86-81037-71-3}, title = {Analysis of COVID-19 disease using machine learning - personalized model}, keyword = {COVID-19, personalized model, clinical condition assessment, ensemble model, rule-based machine learning}, publisher = {University of Kragujevac}, publisherplace = {Kragujevac, Srbija} }




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