Healthc Inform Res.  2013 Jun;19(2):121-129. 10.4258/hir.2013.19.2.121.

Use of Data Mining Techniques to Determine and Predict Length of Stay of Cardiac Patients

• Hachesu PR
• Ahmadi M
• Alizadeh S
• Sadoughi F

• KMID: 2229493
• DOI: http://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 < or =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.

Keyword

Length of Stay; Data Mining; Coronary Artery Disease; Patients; Extract

MeSH Terms

Comorbidity
Coronary Artery Disease
Coronary Vessels
Data Mining
Decision Trees
Heart
Hemorrhage
Humans
Hypertension
Insurance
Length of Stay
Lung
Sensitivity and Specificity
Social Security
Support Vector Machine

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