Implementasi Model Regresi Logistik dalam Klasifikasi Kebutuhan Ruang ICU Terhadap Pasien Positif COVID-19
Keywords:COVID-19, ICU, Data Science, Logistic Regression
Coronavirus Disease 19 (COVID-19) is a type of disease caused by a virus called SARS-CoV-2. The origin of the infection came from Huanan Seafood Market, Wuhan City, Hubei Province, People's Republic of China. The virus attacks the lungs and is indicate to spread to other organs such as the heart, blood vessels, kidneys, intestines, and brain. SARS-CoV-2 virus infection can threaten the life safety of infected patients by attacking the respiratory system and can spread to other organs that trigger comorbidities. The condition of COVID-19 patients with comorbidities is a consideration for ICU admission. Statistics state that 1 in 5 COVID-19 patients undergo treatment in a hospital, and 1 in 10 of them require treatment in the ICU (Intensive Care Unit). In this study, the classification of ICU room needs on COVID-19 patients based on comorbidities and certain conditions using a logistic regression model. Logistic regression implemented with consideration of the data and research variables having a categorical data scale. The data is divide into two, training data and testing data with a ratio of 80%:20%. The purpose of this research is to get the accuracy of the classification. The results showed that the level of accuracy reached 87.29%.
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