Pandemia prediction using machine learning

• Autorzy: Hussein Amir Nasir , Makki Seyed Vahab AL-Din , Al-Sabbagh Ali

Identyfikatory

DOI

10.15199/48.2024.05.39

Warianty tytułu

PL

Przewidywanie pandemii za pomocą uczenia maszynowego

• Języki publikacji: EN

Abstrakty

EN

Coronavirus disease 2019 (COVID-19) is caused a large number of death. Therefore, that Artificial Intelligence (A.I) solution might be capable to identify COVID-19 quickly and early. This paper applies Three ML models to Covid-19 prediction process. We discovered the main dominant variable to decide the negative or positive patient by using different ML models in the prediction process, for instance (LR, XG Boost, and RF). The study and models have been applied for one million patients from European Commission (EC), this data set (cough, fever, sore throat, breath, and headache) been considered as a data sensor coming to the proposed system. The aim is to choose the best ML model for Covid-9 prediction. In addition, all models and dataset have been sufficiently presented with all clarifications and justifications. Also, our data have been provided for one million patients from European Commission (EC). Then, feature selection to prepare the dominant parameters of Cvid-19, which are (cough, fever, sore throat, breath, and headache). As a result, the RF and XG boost obtained the best accuracy in the decision of positive or negative based on nine variables.

PL

Choroba koronawirusowa 2019 (COVID-19) jest przyczyną dużej liczby zgonów. Dlatego to rozwiązanie oparte na sztucznej inteligencji (AI) może być w stanie szybko i wcześnie zidentyfikować Covid-19. W artykule zastosowano trzy modele ML do procesu przewidywania Covid-19. Odkryliśmy główną dominującą zmienną decydującą o tym, czy pacjent jest negatywny, czy pozytywny, stosując w procesie przewidywania różne modele ML, na przykład (LR, XG Boost i RF). Badanie i modele zastosowano w przypadku miliona pacjentów z Komisji Europejskiej (KE). Ten zestaw danych (kaszel, gorączka, ból gardła, oddech i ból głowy) uznano za czujnik danych docierających do proponowanego systemu. Celem jest wybór najlepszego modelu ML do przewidywania Covid-9. Ponadto wszystkie modele i zbiory danych zostały dostatecznie przedstawione ze wszystkimi wyjaśnieniami i uzasadnieniami. Nasze dane dotyczące miliona pacjentów przekazała także Komisja Europejska (KE). Następnie dokonaj selekcji cech, aby przygotować dominujące parametry Cvid-19, którymi są (kaszel, gorączka, ból gardła, oddech i ból głowy). W rezultacie wzmocnienie RF i XG uzyskało najlepszą dokładność w podejmowaniu decyzji pozytywnej lub negatywnej na podstawie dziewięciu zmiennych.

Słowa kluczowe

EN

covid-19; intelligent framework; smart detection; machine learning

PL

covid-19; inteligentne środowisko; inteligentne wykrywanie; uczenie maszynowe

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2024
• Tom: R. 100, nr 5
• Strony: 211--214
• Opis fizyczny: Bibliogr. poz., rys.

Twórcy

autor

Hussein Amir Nasir

• Razi University, Kermanshah, Iran

• https://orcid.org/0009-0002-2596-4685

autor

Makki Seyed Vahab AL-Din

• Razi University, Kermanshah, Iran

• https://orcid.org/0000-0001-7125-8357

autor

Al-Sabbagh Ali

• Al Taff University College, Kerbala, Iraq
• Ministry of communication, ITPC , Iraq

• https://orcid.org/0000-0003-3270-980x

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).