Enhanced Prediction of Intensive Care Unit Length of Stay using a Stack Ensemble of Machine Learning Models

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dc.contributor.authorTella, Ashok Kumar
dc.contributor.authorBalasundaram, S.R
dc.date.accessioned2026-11-06T10:29:15Z
dc.date.available2026-11-06T10:29:15Z
dc.date.issued2024-11-20
dc.description.abstractThe Length of Stay (LoS) refers to the time between a patient's hospital admission and discharge. LoS is considered to increase as the complexity of the disease increases. A prolonged stay in the Intensive Care Unit (ICU) can consume clinical resources and be labor intensive. Models that correctly predict LoS are needed to help medical experts make better decisions. To define an ideal process system, healthcare models must consider the patient's condition, availability of beds, resources, etc. These predictions can also help insurance companies manage their budgets. Existing models deploy machines and deep learning techniques to predict LoS. However, there is a need for improvement, considering the features associated with the process. This study presents machine learning algorithms, such as SVM and a stack ensemble, with improved accuracy over existing models. Experiments were carried out on a benchmark dataset, MIMIC-III, specific to ICU patients. The SVM model achieved an accuracy of 93.88%, while the stack ensemble model showed an improved accuracy of 94.70%. The results show that combining machine learning models achieves better prediction rates, which helps healthcare professionals make better decisions.
dc.identifier.issn22414487
dc.identifier.urihttps://doi.org/10.48084/etasr.8994
dc.identifier.urihttp://nitt.ndl.gov.in/handle/123456789/410
dc.language.isoen
dc.publisherDr D. Pylarinos
dc.relation.ispartofseriesEngineering, Technology and Applied Science Research; Vol. 15/ No. 1
dc.titleEnhanced Prediction of Intensive Care Unit Length of Stay using a Stack Ensemble of Machine Learning Models
dc.typeArticle
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