Prediction of Disease Stage by Machine Learning Classification Methods for Covid-19 Patients
Abstract
Supervised machine learning classificitaion algorithms have been widely used in many fields in recent years.
Especially, health is one of the most important areas where machine learning studies are carried out successfully.
The aim of this study is to develop models that predict the disease stage of people who apply to hospital with the diagnosis of Covid-19.
Inadequacies such as intensive care occupancy, insufficiency of beds, and shortage of respiratory equipment are among these problems, and this has left healthcare workers faced with the overwhelming burden of patients.
Therefore, estimating the disease stages of Covid-19 patients at an early stage is of great importance. The data set used in the study includes the clinical and laboratory data of the patients during in their admission to the hospital.
It has been tried to develop models that predict disease stage by using Logistic Regression, Random Forest and Support Vector Machine algorithms in the data set. The random forest model with 9 variables was the best performing model.
With the models obtained, it will be ensured that the hospital management receives information in order to see the necessary treatment for low-risk or high-risk patients and to avoid medical system inadequacies.
Downloads
How to Cite
Issue
Section
License
The Austrian Journal of Statistics publish open access articles under the terms of the Creative Commons Attribution (CC BY) License.
The Creative Commons Attribution License (CC-BY) allows users to copy, distribute and transmit an article, adapt the article and make commercial use of the article. The CC BY license permits commercial and non-commercial re-use of an open access article, as long as the author is properly attributed.
Copyright on any research article published by the Austrian Journal of Statistics is retained by the author(s). Authors grant the Austrian Journal of Statistics a license to publish the article and identify itself as the original publisher. Authors also grant any third party the right to use the article freely as long as its original authors, citation details and publisher are identified.
Manuscripts should be unpublished and not be under consideration for publication elsewhere. By submitting an article, the author(s) certify that the article is their original work, that they have the right to submit the article for publication, and that they can grant the above license.