Prediction of Disease Stage by Machine Learning Classification Methods for Covid-19 Patients
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...
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| Format: | Article |
| Language: | English |
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Austrian Statistical Society
2024-12-01
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| Series: | Austrian Journal of Statistics |
| Online Access: | https://www.ajs.or.at/index.php/ajs/article/view/1799 |
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| author | Melis Merve Doğançay özlem Ege Oruç Melike Şırlancı Tüysüzoğlu Zeynep Altın |
| author_facet | Melis Merve Doğançay özlem Ege Oruç Melike Şırlancı Tüysüzoğlu Zeynep Altın |
| author_sort | Melis Merve Doğançay |
| collection | DOAJ |
| description |
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.
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| format | Article |
| id | doaj-art-a05d0324c9a345e1a2d28c945c37dd47 |
| institution | Kabale University |
| issn | 1026-597X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Austrian Statistical Society |
| record_format | Article |
| series | Austrian Journal of Statistics |
| spelling | doaj-art-a05d0324c9a345e1a2d28c945c37dd472025-01-13T07:12:26ZengAustrian Statistical SocietyAustrian Journal of Statistics1026-597X2024-12-0153510.17713/ajs.v53i5.1799Prediction of Disease Stage by Machine Learning Classification Methods for Covid-19 PatientsMelis Merve Doğançay0özlem Ege Oruç1Melike Şırlancı Tüysüzoğlu2Zeynep Altın3Dokuz Eylül University, The Graduate School of Natural and Applied SciencesDokuz Eylul University Faculty of SciencesUniversity of Colorado Anschutz Medical CampusHealth Sciences University, Izmir Tepecik Training and Research Hospital 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. https://www.ajs.or.at/index.php/ajs/article/view/1799 |
| spellingShingle | Melis Merve Doğançay özlem Ege Oruç Melike Şırlancı Tüysüzoğlu Zeynep Altın Prediction of Disease Stage by Machine Learning Classification Methods for Covid-19 Patients Austrian Journal of Statistics |
| title | Prediction of Disease Stage by Machine Learning Classification Methods for Covid-19 Patients |
| title_full | Prediction of Disease Stage by Machine Learning Classification Methods for Covid-19 Patients |
| title_fullStr | Prediction of Disease Stage by Machine Learning Classification Methods for Covid-19 Patients |
| title_full_unstemmed | Prediction of Disease Stage by Machine Learning Classification Methods for Covid-19 Patients |
| title_short | Prediction of Disease Stage by Machine Learning Classification Methods for Covid-19 Patients |
| title_sort | prediction of disease stage by machine learning classification methods for covid 19 patients |
| url | https://www.ajs.or.at/index.php/ajs/article/view/1799 |
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