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2000
Volume 18, Issue 7
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Background

Heart failure is the leading cause of death globally over the last several decades. This raises the necessity of timely, accurate, and prudent methods for establishing an early diagnosis and implementing timely illness care.

Objective

This study aims to develop and validate a classification model for the patients admitted to the Intensive Care Unit (ICU) with heart failure, using various machine learning models applied to the MIMIC (Medical Information Mart for Intensive Care)-III database.

Methods

A retrospective cohort study was conducted using data extracted from the MIMIC-III database. Machine learning models: Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, Decision Tree, Naïve Bayes, AdaBoost, and XGBoost were utilized to construct the predictive model. The dataset has been preprocessed in two different manners. The study included 1,177 patients with heart failure, selected according to specific inclusion/exclusion criteria and admitted to the ICU.

Results

At the end of the study, the most effective model for predicting patients who survived was Logistic Regression, with an accuracy of 0.9025, sensitivity of 0.9763, precision of 0.9196, and F1-score of 0.9471.

Conclusion

Classification of the patients into those who survived or could not survive due to heart failure was the primary measure, with various clinical and demographic variables used as predictors.

© 2025 Bentham Science Publishers
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2024-05-09
2025-09-26

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References

  1. SavareseG. BecherP.M. LundL.H. SeferovicP. RosanoG.M.C. CoatsA.J.S. Global burden of heart failure: A comprehensive and updated review of epidemiology.Cardiovasc. Res.2023118173272328710.1093/cvr/cvac013 35150240
     [Google Scholar]
  2. HeidenreichP.A. FonarowG.C. OpshaY. SandhuA.T. SweitzerN.K. WarraichH.J. ButlerJ. HsichE. PresslerS.B. ShahK. TaylorK. SabeM. NgT. Economic issues in heart failure in the United States.J. Card. Fail.202228345346610.1016/j.cardfail.2021.12.017 35085762
     [Google Scholar]
  3. BytyçiI. BajraktariG. Mortality in heart failure patients.Anadolu Kardiyol. Derg.2015151636810.5152/akd.2014.5731 25550250
     [Google Scholar]
  4. GlynnP.A. MolsberryR. HarringtonK. ShahN.S. PetitoL.C. YancyC.W. CarnethonM.R. Lloyd‐JonesD.M. KhanS.S. Geographic variation in trends and disparities in heart failure mortality in the United States, 1999 to 2017.J. Am. Heart Assoc.202110e020541
     [Google Scholar]
  5. RogerV.L. Epidemiology of heart failure: A contemporary perspective.Circ. Res.2021128101421143410.1161/CIRCRESAHA.121.318172 33983838
     [Google Scholar]
  6. LiF. XinH. ZhangJ. FuM. ZhouJ. LianZ. Prediction model of in-hospital mortality in intensive care unit patients with heart failure: Machine learning-based, retrospective analysis of the MIMIC-III database.BMJ Open2021117e04477910.1136/bmjopen‑2020‑044779 34301649
     [Google Scholar]
  7. LiJ. LiuS. HuY. ZhuL. MaoY. LiuJ. Predicting mortality in intensive care unit patients with heart failure using an interpretable machine learning model: Retrospective cohort study.J. Med. Internet Res.2022248e3808210.2196/38082 35943767
     [Google Scholar]
  8. JentzerJ.C. ReddyY.N. RosenbaumA.N. DunlayS.M. BorlaugB.A. HollenbergS.M. Outcomes and predictors of mortality among cardiac intensive care unit patients with heart failure.J. Card. Fail.20222871088109910.1016/j.cardfail.2022.02.015 35381356
     [Google Scholar]
  9. AliA. AsgharS.Y. AliD.K.Y. Prediction of in-hospital mortality among heart failure patients: An automated machine learning analysis of mimic-III database.American Heart J.2022254261
     [Google Scholar]
  10. JiX. KeW. Red blood cell distribution width and all-cause mortality in congestive heart failure patients: A retrospective cohort study based on the Mimic-III database.Front. Cardiovasc. Med.202310112671810.3389/fcvm.2023.1126718 37206106
     [Google Scholar]
  11. WangR. CaiL. LiuY. ZhangJ. OuX. XuJ. Machine learning algorithms for prediction of ventilator associated pneumonia in traumatic brain injury patients from the MIMIC-III database.Heart Lung202362225232
     [Google Scholar]
  12. LohW.Y. Logistic regression tree analysis.Springer handbook of engineering statistics.LondonSpringer London202359360410.1007/978‑1‑4471‑7503‑2_30
     [Google Scholar]
  13. DinataR.K. AdekR.T. HasdynaN. RetnoS. K-nearest neighbor classifier optimization using purity AIP Conference ProceedingsAIP Publishing202324311
     [Google Scholar]
  14. A.L.Yadav K.Soni S.Khare Heart diseases prediction using machine learning2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) 06-08 July 2023, Delhi, India, 202310.1109/ICCCNT56998.2023.10306469
     [Google Scholar]
  15. ZermaneA. TohirM.M.Z. ZermaneH. BaharudinM.R. YusoffM.H. Predicting fatal fall from heights accidents using random forest classification machine learning model.Saf. Sci.202315910602310.1016/j.ssci.2022.106023
     [Google Scholar]
  16. AsselmanA. KhaldiM. AammouS. Enhancing the prediction of student performance based on the machine learning XGBoost algorithm.Interact. Learn. Environ.20233163360337910.1080/10494820.2021.1928235
     [Google Scholar]
  17. RamakrishnaM.T. VenkatesanV.K. IzoninI. HavryliukM. BhatC.R. Homogeneous adaboost ensemble machine learning algorithms with reduced entropy on balanced data.Entropy202325224510.3390/e25020245 36832611
     [Google Scholar]
  18. SulaimanA. KaurS. GuptaS. AlshahraniH. ReshanM.S.A. AlyamiS. ShaikhA. ResRandSVM: Hybrid approach for acute lymphocytic leukemia classification in blood smear images.Diagnostics20231312212110.3390/diagnostics13122121 37371016
     [Google Scholar]
  19. KaurS. GuptaS. SinghS. HoangV.T. AlmakdiS. AlelyaniT. ShaikhA. Transfer learning-based automatic hurricane damage detection using satellite images.Electronics2022119144810.3390/electronics11091448
     [Google Scholar]
  20. KaurS. GuptaS. SinghS. GuptaI. Detection of Alzheimer’s disease using deep convolutional neural network.Int. J. Image Graph.2022223214001210.1142/S021946782140012X
     [Google Scholar]
  21. KaurS. GuptaS. SinghS. AroraT. A review on natural disaster detection in social media and satellite imagery using machine learning and deep learning.Int. J. Image Graph.2022225225004010.1142/S0219467822500401
     [Google Scholar]
  22. S.Singh K.R.Ramkumar A.Kukkar Machine learning techniques and implementation of different ML algorithms2021 2nd Global Conference for Advancement in Technology (GCAT) 01-03 October 2021, Bangalore, India, 2021.10.1109/GCAT52182.2021.9586806
     [Google Scholar]
  23. K.S.Gill V.Anand R.Gupta C.Khosla P.A.Hsiung A new approach towards deployment and management of machine learning models using K-serve platform for building robust machine learning systems4th IEEE Global Conference for Advancement in Technology (GCAT) pp.1-5, 2023.
     [Google Scholar]
  24. DharmarathneG. JayasingheT.N. BogahawaththaM. MeddageD.P.P. RathnayakeU. A novel machine learning approach for diagnosing diabetes with a self-explainable interface.Healthcar. Analyt.2024510030110.1016/j.health.2024.100301
     [Google Scholar]
  25. MeddageD.P.P. EkanayakeI.U. HerathS. GobirahavanR. MuttilN. RathnayakeU. Predicting bulk average velocity with rigid vegetation in open channels using tree-based machine learning: A novel approach using explainable artificial intelligence.Sensors20222212439810.3390/s22124398 35746184
     [Google Scholar]
  26. MeddageP. EkanayakeI. PereraU.S. AzamathullaH.M. Md SaidM.A. RathnayakeU. Interpretation of machine-learning-based (black-box) wind pressure predictions for low-rise gable-roofed buildings using Shapley additive explanations (SHAP).Buildings202212673410.3390/buildings12060734
     [Google Scholar]
  27. BarrettL.A. PayrovnaziriS.N. BianJ. HeZ. Building computational models to predict one-year mortality in ICU patients with acute myocardial infarction and post myocardial infarction syndrome.AMIA Jt. Summits Transl. Sci. Proc.20192019407416 31258994
     [Google Scholar]
  28. LinK. HuY. KongG. Predicting in-hospital mortality of patients with acute kidney injury in the ICU using random forest model.Int. J. Med. Inform.2019125556110.1016/j.ijmedinf.2019.02.002 30914181
     [Google Scholar]
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A Comparative Analysis of Machine Learning Models for the Classification of Heart Failure Patients in the Intensive Care Unit
Recent Advances in Electrical & Electronic Engineering 18, 834 (2025); https://doi.org/10.2174/0123520965312805240506113451
/content/journals/raeeng/10.2174/0123520965312805240506113451
/content/journals/raeeng/10.2174/0123520965312805240506113451
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  • Article Type:     Research Article
Keyword(s): AdaBoost; decision tree; Heart failure; intensive care unit; KNN; logistic regression; machine learning; MIMIC-III; naïve bayes; random forest; XGBoost

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