Volume 16, Issue 2 (March & April-In Press 2026)
J Research Health 2026, 16(2): 11-11 |
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Ethics code: IR.MUMS.FHMPM.REC.1402.214
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sabouri S, Salari M. Predicting Hospital Length of Stay in Cardiovascular Disease Patients using Count Regressions and Machine Learning Approaches. J Research Health 2026; 16 (2) :11-11
URL: http://jrh.gmu.ac.ir/article-1-2713-en.html
URL: http://jrh.gmu.ac.ir/article-1-2713-en.html
1- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran. & Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. , sabourism@mums.ac.ir
2- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran. & Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
2- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran. & Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Abstract: (12 Views)
Cardiovascular diseases often involve hospital readmissions and prolonged length of stay (LOS), a key measure for evaluating hospital care efficiency and quality. This cross-sectional study aimed to predict LOS and identify its associated factors. A total of 12,752 patients with heart diseases admitted to 43 hospitals affiliated with Mashhad University of Medical Sciences (MUMS) between March 2020 and February 2021 were included. The mean patient age was 62.7 ± 13.6 years, with an average LOS of 4.4 ± 5.7 days and a median of 3 days (interquartile range [IQR]: 3(. Age, sex, diagnosis type, and percutaneous coronary intervention (PCI) were analyzed to predict LOS. Various count regression models and machine learning (ML) approaches were evaluated, and predictive performance was assessed using mean absolute error (MAE). Zero-truncated negative binomial regression (ZTNBR) outperformed other count regression models in the evaluation. Among ML models, support vector machine (SVM) achieved the lowest MAE (2.6), outperforming artificial neural network (ANN) (2.9) and ZTNBR (3.0). The type of cardiovascular disease was the most significant predictor of LOS, followed by PCI, age, and sex. These findings highlight the importance of patient-specific factors in designing healthcare strategies to optimize hospital resource allocation and improve patient outcomes.
Type of Study: Short Communication |
Subject:
● Service Quality
Received: 2024/12/27 | Accepted: 2025/06/25 | Published: 2026/03/21
Received: 2024/12/27 | Accepted: 2025/06/25 | Published: 2026/03/21
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