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001-es BibID:	BIBFORM122234
035-os BibID:	(Scopus)85128327615
Első szerző:	Ahmed, Alaa H.
Cím:	Prediction of COVID-19 disease severity using machine learning techniques / Ahmed, Alaa H. ; Al-Hamadani, Mokhaled N. A. ; Satam, Ihab A.
Dátum:	2022
ISSN:	2089-3191 2302-9285
Megjegyzések:	A terrifying spread of COVID-19 (which is also known as severe acute respiratory syndrome coronavirus 2 or SARS-COV-2) led scientists to conduct tremendous efforts to reduce the pandemic effects. COVID-19 has been announced pandemic discovered in 2019 and affected millions of people. Infected people may experience headache, body pain, and sometimes difficulty in breathing. For older people, the symptoms can get worse. Also, it can cause death because of the huge effect on some parts of the human body, particularly for those who have chronic diseases like diabetes. Machine learning algorithms are applied to patients diagnosed with Corona Virus to estimate the severity of the disease depending on their chronic diseases at an early stage. Chronic diseases could raise the severity of COVID-19 and that is what has been proved in this paper. This paper applies different machine learning techniques such as random forest, decision tree, linear regression, binary search, and k-nearest neighbor on Mexican patients' dataset to find out the impact of lifelong illnesses on increasing the symptoms of the virus in the human body. Besides, the paper demonstrates that in some cases, especially for older people, the virus can cause inevitable death.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Chronic Diseases Classification Covid-19 Machine Learning Severity
Megjelenés:	Bulletin of Electrical Engineering and Informatics. - 11 : 2 (2022), p. 1069-1074. -
További szerzők:	Al-Hamadani, Mokhaled N. A. (1987-) (informatics) Satam, Ihab Abdulrahman
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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