Kalp Yetersizliği Olan Hastaların Hastaneye Yeniden Yatışı İle İlgili Faktörlerin Veri Madenciliği Teknikleri İle İncelenmesi
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The high levels of rehospitalization is a sign of deficient providence of the necessary health services at the hospitals. Unplanned rehospitalization cause unnecessary expenses in the health sector and also impact the quality of the patient negatively. Due to these reasons, the health managers have been trying to develop policies to reduce those unplanned rehospitalizations. With this study, the prediction of the factors that cause the rehospitalization of the patients who had to be rehospitalized at the Health Sciences University Ümraniye Training and Research Hospital within 30 days of being discharged by using data mining application. The researcher has worked with the data of a total of 400 people composed of 200 patients who had been hospitalized at the Ümraniye Training and Research Hospital between the dates of 29.08.2008 and 10.04.2014 and had to be rehospitalized within 30 days after their discharge from the hospital, and 200 patients who had not been rehospitalized within 30 days after their discharge from the hospital. The patient background data used in the study are the following: Age, gender, their level of calcium, urea, creatinine, potassium and sodium, comorbidity, their number of outpatient clinic visits in the last year, and number of emergency service visits. The application section of this study has been made with the Weka Program which is one of the package programs used in relation to machine learning. The model has been formed by the J.48 Algorithm of the decision tree which is one of the classification methods of data mining. According to the prediction of the created model, the number of correct predictions out of 400 is 299 (74.75%). The number of correct predictions with the rehospitalized 200 patients is 147, and with the non-rehospitalized patients is 152. The values of validity and reliability of the model is found to be 74.8%.