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Use of data mining techniques to determine and predict length of stay of cardiac patients
Peyman Rezaei Hachesu1, Maryam Ahmadi, Somayyeh Alizadeh
1Department of Health Information Management, School of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Insights
Predicting hospital length of stay (LOS) for heart patients using data mining can improve patient care and hospital management. Support vector machines (SVM) achieved 96.4% accuracy, identifying factors like comorbidities that influence LOS.
Area of Science:
- Medical Informatics
- Data Mining
- Cardiology
Background:
- Accurate prediction of hospital length of stay (LOS) is crucial for optimizing patient services, satisfaction, and hospital resource management.
- Data mining offers powerful tools for extracting actionable insights from complex patient data.
Purpose of the Study:
- To develop and evaluate data mining models for predicting the LOS of patients with coronary artery disease (CAD).
- To identify key patient factors influencing LOS for better healthcare planning.
Main Methods:
- Analysis of 4,948 patient records with coronary artery disease (CAD).
- Application of classification algorithms: decision tree, support vector machines (SVM), and artificial neural network (ANN).
- LOS as the target variable, utilizing 36 input variables for prediction and evaluating accuracy via a confusion matrix.
Main Results:
- Support vector machines (SVM) demonstrated the highest prediction accuracy at 96.4% on the training set.
- Patient demographics (marital status) and clinical factors (comorbidities like lung disorders, ejection fraction <2, smoking status, insurance type) significantly impacted LOS.
- Married patients and those with specific comorbidities experienced longer hospital stays.
Conclusions:
- All three data mining algorithms effectively predicted LOS, with SVM being the most accurate.
- Lung or respiratory disorders and high blood pressure were identified as significant predictors of longer LOS in heart patients.
- The study highlights the utility of data mining in predicting patient LOS and informing clinical and administrative decisions.
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.
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