Predicting Readmission of Cardiovascular Patients Admitted to the CCU using Data Mining Techniques

The Open Cardiovascular Medicine Journal 14 July 2023 RESEARCH ARTICLE DOI: 10.2174/18741924-v17-e230627-2022-21



Cardiovascular (CV) diseases account for a large number of readmissions.


Using data mining techniques, we aimed to predict the readmission of CV patients to Coronary Care Units of 4 public hospitals in Shiraz, Iran, within 30 days after discharge.


To identify the variables affecting the readmission of CV patients in the present cross-sectional study, a comprehensive review of previous studies and the consensus of specialists and sub-specialists were used. The obtained variables were based on 264 readmitted and non-readmitted patients. Readmission was modeled with predictive algorithms with an accuracy of >70% using the IBM SPSS Modeler 18.0 software. Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology provided a structured approach to planning the project.


Overall, 47 influential variables were included. The Support Vector Machine (SVM), Chi-square Automatic Interaction Detection (CHIAD), artificial neural network, C5.0, K-Nearest Neighbour, logistic regression, Classification and Regression (C&R) tree, and Quest algorithms with an accuracy of 98.60%, 89.60%, 89.90%, 88.00%, 85.90%, 79.90%, 78.60%, and 74.40%, respectively, were selected. The SVM algorithm was the best model for predicting readmission. According to this algorithm, the factors affecting readmission were age, arrhythmia, hypertension, chest pain, type of admission, cardiac or non-cardiac comorbidities, ejection fraction, undergoing coronary angiography, fluid and electrolyte disorders, and hospitalization 6-9 months before the current admission.


According to the influential variables, it is suggested to educate patients, especially the older ones, about following physician advice and also to teach medical staff about up-to-date options to reduce readmissions.

Keywords: Forecasting, Patient readmission, Cardiovascular diseases, Coronary care units, Data mining, Physicians.
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