Decision Tree for Determining Hospital Treatment for Covid-19 Patients Based on Hematology Parameters Using the C5.0 Algorithm - Riyono, Joko; Pujiastuti, Christina Eni; Supriyadi, Supriyadi; Prayitno, Dody; Putri, Aina Latifa Riyana (Universitas Trilogi, 2024)
The rapid spread of the COVID-19 disease, which occurred globally from late 2019 to the early 2020s, significantly impacted communities worldwide, requires early detection of COVID-19 which is very important for patients and also the people around them to be able to fight the COVID-19 pandemic. Therefore, a classification analysis will be carried out to make decisions regarding determining COVID-19 patients who do not require hospitalization or who require Regular Ward, Semi-Intensive Care Unit, or Intensive Care Unit (ICU) in hospitals based on hematology parameters from the Machine Learning Repository. Kaggle Dataset uses the C5.0 algorithm assisted by Rstudio software. It is also known that because the data contains missing data, it is also necessary to handle missing data using the Mean Method assisted by SPSS software. Performance evaluated using the Confusion Matrix method produces an accuracy value of 78% which is considered quite good, where testing with the C5.0 Algorithm uses a training and testing data ratio of 40:60. This research simplifies and speeds up medical decision-making, improving patient management. With COVID-19 declining, the method can be applied to enhance healthcare systems' accuracy and efficiency in handling other diseases or emergencies, ensuring better preparedness for future challenges.