Use of Data Mining Techniques to Determine and Predict Length of Stay of Cardiac Patients
Open Access
- 31 May 2013
- journal article
- research article
- Published by The Korean Society of Medical Informatics in Healthcare Informatics Research
- Vol. 19 (2), 121-129
- https://doi.org/10.4258/hir.2013.19.2.121
Abstract
Objectives: Predicting the length of stay (LOS) of patients in a hospital is important in providing them with better services and higher satisfaction, as well as helping the hospital management plan and managing hospital resources as meticulously as possible. We propose applying data mining techniques to extract useful knowledge and draw an accurate model to predict the LOS of heart patients. Methods: Data were collected from patients with coronary artery disease (CAD). The patient records of 4,948 patients who had suffered CAD were included in the analysis. The techniques used are classification with three algorithms, namely, decision tree, support vector machines (SVM), and artificial neural network (ANN). LOS is the target variable, and 36 input variables are used for prediction. A confusion matrix was obtained to calculate sensitivity, specificity, and accuracy. Results: The overall accuracy of SVM was 96.4% in the training set. Most single patients (64.3%) had an LOS <= 5 days, whereas 41.2% of married patients had an LOS > 10 days. Moreover, the study showed that comorbidity states, such as lung disorders and hemorrhage with drug consumption have an impact on long LOS. The presence of comorbidities, an ejection fraction < 2, being a current smoker, and having social security type insurance in coronary artery patients led to longer LOS than other subjects. Conclusions: All three algorithms are able to predict LOS with various degrees of accuracy. The findings demonstrated that the SVM was the best fit. There was a significant tendency for LOS to be longer in patients with lung or respiratory disorders and high blood pressure.This publication has 23 references indexed in Scilit:
- Analysis Of Heart Diseases Dataset Using Neural Network ApproachInternational Journal of Data Mining & Knowledge Management Process, 2011
- Prediction of Length of Stay Following Elective Percutaneous Coronary InterventionISRN Surgery, 2011
- Identifying factors that impact patient length of stay metrics for healthcare providers with advanced analyticsHealth Informatics Journal, 2010
- Application of Support Vector Machine for Prediction of Medication Adherence in Heart Failure PatientsHealthcare Informatics Research, 2010
- Prediction of Hospital Charges for the Cancer Patients with Data Mining TechniquesJournal of Korean Society of Medical Informatics, 2009
- Predictive data mining in clinical medicine: Current issues and guidelinesInternational Journal of Medical Informatics, 2008
- The use of artificial neural networks to stratify the length of stay of cardiac patients based on preoperative and initial postoperative factorsArtificial Intelligence in Medicine, 2007
- Prediction of length of stay for stroke patientsActa Neurologica Scandinavica, 2006
- Resource utilisation, length of hospital stay, and pattern of investigation during acute medical hospital admissionHeart, 2004
- Coronary Artery Calcification: Pathophysiology, Epidemiology, Imaging Methods, and Clinical ImplicationsCirculation, 1996