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Machine Learning Techniques for Predicting Outcomes of COVID-19 for Patients with preexisting Chronic Diseases
International Conference on Medical and Biological Engineering in Bosnia and Herzegovina, CMBEBIH 2021 ; 84:867-882, 2021.
Article in English | Scopus | ID: covidwho-1340338
ABSTRACT
COVID-19 was officially confirmed during December 2019 in the city of Wuhan, China, while the first case of the disease was recorded on 17 November 2019. The World Health Organization has declared a pandemic due to the rapid spread of this disease. It is believed, due to worldwide population aging, that middle-aged and geriatric patients who suffer from chronic diseases are more prone to respiratory failure and having a poorer outcome caused by COVID-19. This paper presents the association of certain chronic diseases such as diabetes mellitus, COPD, hypertension, asthma, and others with COVID-19. Testing was done on 400 samples who were positive for the virus, 250 samples were sick of some of the listed diseases, while 150 were healthy. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Specific parameters for different diseases were used in the paper. Based on the results presented in the paper, we concluded that chronic diseases greatly affect the number of people infected with COVID-19. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: International Conference on Medical and Biological Engineering in Bosnia and Herzegovina, CMBEBIH 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: International Conference on Medical and Biological Engineering in Bosnia and Herzegovina, CMBEBIH 2021 Year: 2021 Document Type: Article