Predict Admission of Confirmed COVID-19 Cases to ICU
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Abstract
Health systems all throughout the world have been impacted by the most recent COVID-19 pandemic. Critically sick patients have received crucial care in intensive care units (ICUs), in particular. The quick spread of the virus has increased admissions, but this has also created a number of issues for ICU wards, including a lack of ICU beds, a staff that is overworked caring for patients, and a lack of medical resources to treat everyone in hospitals. These problems may have had a direct impact on a patient's survival by lowering the quality of healthcare services offered. The project's goal is to predict the admission of covid-19 patients to ICU because we already have a shortage of beds in ICU to treat severely affected patients. This application assists hospitals in admitting critical patients only to ICU by analyzing their reports, which include various attributes collected from the patients such as temperature difference, age, blood pressure, heart rate, respiratory rate, oxygen saturation, and a few other attributes. The accurate prediction is challenging task so we use possible machine learning techniques such as logistic regression, Gaussian Naïve Bayes, SGD classifier (Stochastic gradient descent) and XGB Regressor(Extreme Gradient Boosting) and compare the performances of individual model based upon the metrics such as accuracy, precision, ROC curve, F1 score and others to implement the application with better model.
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