Examination of inpatient COVID-19 patient data in Geyve State Hospital by data mining method

The coronavirus has spread rapidly in our country as well as all over the world, negatively affecting people's health. Among the people who got the disease in our country, there were people who had to stay in the hospital as well as those who survived the disease mildly. Keeping the data of people who have been infected and hospitalized has become an important parameter for Covid-19 services. In this study; In order to determine the common characteristics of patients hospitalized in the Covid-19 service of Geyve State Hospital between April 2, 2020 and September 30, 2021, the data of the patients were examined. The Apriori algorithm, which is one of the association analysis methods, was applied to the obtained dataset and the association rules of the people who were infected with the Covid-19 disease were deduced. For this, the number of vaccines, vaccine type, age, gender, length of hospital stay and discharge type parameters of the patients are one of the data mining software. Examined using RapidMiner application. As a result of this study; Inferences were made about the relationships between the number of vaccines, type of vaccine, age, gender, length of hospital stay and type of discharge from the hospital. Examining patient profiles and predicting how long the patients will stay in the hospital can be important in terms of planning in inpatient services. In this study, the data of 556 patients taken from the Sakarya Geyve State Hospital COVID-19 inpatient service were analyzed with the apriori algorithm, which is one of the association rule analysis methods, and association rules were extracted. As a result of the analysis, 506 of the hospitalized patients were discharged from the hospital in good health. It was observed that 198 patients who preferred to receive two Biontech vaccines were not vaccinated when they caught Covid-19. 292 patients were hospitalized for only 0-7 days. Only 1 person was hospitalized for more than 30 days. Hospitalization was mostly between the ages of 45-74. It is thought that the information obtained as a result of the study will benefit the hospital management and doctors and contribute positively to the service received by the patients.