Publication date: Jul 01, 2024
Death due to covid-19 is one of the biggest health challenges in the world. There are many models that can predict death due to COVID-19. This study aimed to fit and compare Decision Tree (DT), Support Vector Machine (SVM), and AdaBoost models to predict death due to COVID-19. To describe the variables, mean (SD) and frequency (%) were reported. To determine the relationship between the variables and the death caused by COVID-19, chi-square test was performed with a significance level of 0. 05. To compare DT, SVM and AdaBoost models for predicting death due to COVID-19 from sensitivity, specificity, accuracy and the area under the rock curve under R software using psych, caTools, random over-sampling examples, rpart, rpartplot packages was done. Out of the total of 23,054 patients studied, 10,935 cases (46. 5%) were women, and 12,569 cases (53. 5%) were men. Additionally, the mean age of the patients was 54. 9 +/- 21. 0 years. There is a statistically significant relationship between gender, fever, cough, muscle pain, smell and taste, abdominal pain, nausea and vomiting, diarrhea, anorexia, dizziness, chest pain, intubation, cancer, diabetes, chronic blood disease, Violation of immunity, pregnancy, Dialysis, chronic lung disease with the death of covid-19 patients showed (p
| Concepts | Keywords |
|---|---|
| Cancer | AdaBoost |
| Death | COVID‐19 |
| Diabetes | data mining models |
| Models | death |
| Decision Tree | |
| effective factors | |
| Support Vector Machine |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | death |
| disease | MESH | COVID-19 |
| disease | VO | frequency |
| disease | MESH | anorexia |
| disease | MESH | chest pain |
| disease | MESH | cancer |
| disease | MESH | blood disease |
| disease | MESH | Long Covid |
| disease | VO | effective |