Volume 28, Issue 2 (9-2025)
jha 2025, 28(2): 53-69 |
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Esmaeili M, Lotfnezhad Afshar H, Rahimi B, Khademvatani K, Samadzad Qushchi S, Hoseinpour V. Predicting the length of hospital stay in patients with congestive heart failure using data mining techniques. jha 2025; 28 (2) :53-69
URL: http://jha.iums.ac.ir/article-1-4586-en.html
URL: http://jha.iums.ac.ir/article-1-4586-en.html
Mozhgan Esmaeili1 
, Hadi Lotfnezhad Afshar2 , Bahlol Rahimi3 , Kamal Khademvatani4 , Shirin Samadzad Qushchi5 , Vahid Hoseinpour6
, Hadi Lotfnezhad Afshar2 , Bahlol Rahimi3 , Kamal Khademvatani4 , Shirin Samadzad Qushchi5 , Vahid Hoseinpour6
1- Department of Medical Informatics, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran.
2- Department of Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran. & Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran. ,hadi.afshar@gmail.com
3- Department of Medical Informatics, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran. & Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran.
4- Department of Cardiology, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran.
5- Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran.
6- Department of Emergency Medicine, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran.
2- Department of Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran. & Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran. ,
3- Department of Medical Informatics, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran. & Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran.
4- Department of Cardiology, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran.
5- Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran.
6- Department of Emergency Medicine, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran.
Abstract: (659 Views)
Introduction: Congestive heart failure (CHF) is a significant global challenge for healthcare systems, with its prevalence increasing due to an aging population. Accurate prediction of the length of stay (LOS) for CHF patients is critical for optimizing hospital resource management, reducing treatment costs, and improving the quality of medical care. This study aimed to develop a data mining-based predictive model to estimate the LOS of CHF patients and identify the most influential factors.
Methods: This cross-sectional study was conducted using the data of 3,421 CHF patients hospitalized at Seyed Al-Shohada and Ayatollah Taleghani hospitals in Urmia, Iran, between 2018 and 2020. Data from Seyed Al-Shohada Hospital were used for model training (80%) and testing (20%). The LOS was categorized into short-term and long-term classes using K-means clustering. Random forest, Decision tree (C5.0), Artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) were applied to classify LOS. Techniques such as oversampling, undersampling, and SMOTE were applied to balance the classes, and 10-fold cross-validation was used to ensure model reliability. The Apriori algorithm was also used to discover association rules.
Results: The random forest achieved the best performance with an accuracy of 87.14%, a sensitivity of 97.56%, and an AUC of 85.40%. Key predictors of LOS included elevated creatinine levels, low hemoglobin, male gender, and underlying comorbidities. The Apriori algorithm also revealed significant clinical and meaningful associations among variables.
Conclusion: The proposed model can serve as an effective tool for predicting LOS in CHF patients and support clinical and administrative decision-making in hospital settings.
Methods: This cross-sectional study was conducted using the data of 3,421 CHF patients hospitalized at Seyed Al-Shohada and Ayatollah Taleghani hospitals in Urmia, Iran, between 2018 and 2020. Data from Seyed Al-Shohada Hospital were used for model training (80%) and testing (20%). The LOS was categorized into short-term and long-term classes using K-means clustering. Random forest, Decision tree (C5.0), Artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) were applied to classify LOS. Techniques such as oversampling, undersampling, and SMOTE were applied to balance the classes, and 10-fold cross-validation was used to ensure model reliability. The Apriori algorithm was also used to discover association rules.
Results: The random forest achieved the best performance with an accuracy of 87.14%, a sensitivity of 97.56%, and an AUC of 85.40%. Key predictors of LOS included elevated creatinine levels, low hemoglobin, male gender, and underlying comorbidities. The Apriori algorithm also revealed significant clinical and meaningful associations among variables.
Conclusion: The proposed model can serve as an effective tool for predicting LOS in CHF patients and support clinical and administrative decision-making in hospital settings.
Keywords: Congestive heart failure, Length of stay, Data mining, Machine learning, Random forest, Association rule mining
Type of Study: Research |
Subject:
Health Information Technology
Received: 2025/02/9 | Accepted: 2025/09/3 | Published: 2025/09/28
Received: 2025/02/9 | Accepted: 2025/09/3 | Published: 2025/09/28
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