Abstract
The first instance of COVID-19 was found in Wuhan, China, which mainly caused damage to human body in the form of respiratory diseases. In this study, an XGBoost prediction model was put forward according to the analysis on age, pneumonia, diabetes, and other attributes in the dataset, which was employed to estimate the COVID-19 patients' risk of death. In this study, a lot of preprocessing was carried out on the dataset, such as deleting null values in the dataset. In addition, there are strong correlation between sex, pnueumonia and death probability. In this study, XGBoost, CatBoost, logistic regression and random forest were established by machine learning method to forecast the COVID-19 patients' chance of mortality. The findings revealed that XGBoost's prediction performance was the best, while the logistic regression model performed poorly in this reported dataset of COVID-19 patients when compared to other approaches. From the feature importance map of XGBoost, it is found that age and pneumonia have great influence on the prediction of death risk.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
