RESEARCH ARTICLE


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



Marzie Salimi1, #, Peivand Bastani2, #, Mahdi Nasiri3, Mehrdad Karajizadeh4, #, Ramin Ravangard5, *
1 Student Research Committee, Department of Health Services Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
2 College of Health and Human Sciences, Charles Darwin University, Alice Springs, NT, 0870, Australia
3 Research Center for Design and Fabrication of Advanced Electronic Devices, Shiraz, Iran
4 Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
5 Health Human Resources Research Center, Department of Health Services Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran


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Creative Commons License
© 2023 Salimi et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Health Services Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran; Tel: 00987132340774; E-mail: ra_ravangard@yahoo.com
#These authors have contributed equally to this work.


Abstract

Background:

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

Objective:

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.

Methods:

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.

Results:

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.

Conclusion:

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.