Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure
Cardiovascular diseases have been identified as one of the top three causes of death worldwide, with onset and deaths mostly due to heart failure (HF). In ICU, where patients with HF are at increased risk of death and consume significant medical resources, early and accurate prediction of the time o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/11/21/6460 |
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| author | Chih-Chou Chiu Chung-Min Wu Te-Nien Chien Ling-Jing Kao Chengcheng Li Han-Ling Jiang |
| author_facet | Chih-Chou Chiu Chung-Min Wu Te-Nien Chien Ling-Jing Kao Chengcheng Li Han-Ling Jiang |
| author_sort | Chih-Chou Chiu |
| collection | DOAJ |
| description | Cardiovascular diseases have been identified as one of the top three causes of death worldwide, with onset and deaths mostly due to heart failure (HF). In ICU, where patients with HF are at increased risk of death and consume significant medical resources, early and accurate prediction of the time of death for patients at high risk of death would enable them to receive appropriate and timely medical care. The data for this study were obtained from the MIMIC-III database, where we collected vital signs and tests for 6699 HF patient during the first 24 h of their first ICU admission. In order to predict the mortality of HF patients in ICUs more precisely, an integrated stacking model is proposed and applied in this paper. In the first stage of dataset classification, the datasets were subjected to first-level classifiers using RF, SVC, KNN, LGBM, Bagging, and Adaboost. Then, the fusion of these six classifier decisions was used to construct and optimize the stacked set of second-level classifiers. The results indicate that our model obtained an accuracy of 95.25% and AUROC of 82.55% in predicting the mortality rate of HF patients, which demonstrates the outstanding capability and efficiency of our method. In addition, the results of this study also revealed that platelets, glucose, and blood urea nitrogen were the clinical features that had the greatest impact on model prediction. The results of this analysis not only improve the understanding of patients’ conditions by healthcare professionals but allow for a more optimal use of healthcare resources. |
| first_indexed | 2024-03-09T18:58:03Z |
| format | Article |
| id | doaj.art-cbdd1cf46e044a199fd51810fc798692 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T18:58:03Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-cbdd1cf46e044a199fd51810fc7986922023-11-24T05:18:01ZengMDPI AGJournal of Clinical Medicine2077-03832022-10-011121646010.3390/jcm11216460Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart FailureChih-Chou Chiu0Chung-Min Wu1Te-Nien Chien2Ling-Jing Kao3Chengcheng Li4Han-Ling Jiang5Department of Business Management, National Taipei University of Technology, Taipei 106, TaiwanDepartment of Business Management, National Taipei University of Technology, Taipei 106, TaiwanCollege of Management, National Taipei University of Technology, Taipei 106, TaiwanDepartment of Business Management, National Taipei University of Technology, Taipei 106, TaiwanCollege of Management, National Taipei University of Technology, Taipei 106, TaiwanAlliance Manchester Business School, University of Manchester, Manchester M15 6PB, UKCardiovascular diseases have been identified as one of the top three causes of death worldwide, with onset and deaths mostly due to heart failure (HF). In ICU, where patients with HF are at increased risk of death and consume significant medical resources, early and accurate prediction of the time of death for patients at high risk of death would enable them to receive appropriate and timely medical care. The data for this study were obtained from the MIMIC-III database, where we collected vital signs and tests for 6699 HF patient during the first 24 h of their first ICU admission. In order to predict the mortality of HF patients in ICUs more precisely, an integrated stacking model is proposed and applied in this paper. In the first stage of dataset classification, the datasets were subjected to first-level classifiers using RF, SVC, KNN, LGBM, Bagging, and Adaboost. Then, the fusion of these six classifier decisions was used to construct and optimize the stacked set of second-level classifiers. The results indicate that our model obtained an accuracy of 95.25% and AUROC of 82.55% in predicting the mortality rate of HF patients, which demonstrates the outstanding capability and efficiency of our method. In addition, the results of this study also revealed that platelets, glucose, and blood urea nitrogen were the clinical features that had the greatest impact on model prediction. The results of this analysis not only improve the understanding of patients’ conditions by healthcare professionals but allow for a more optimal use of healthcare resources.https://www.mdpi.com/2077-0383/11/21/6460heart failuremachine learningstackingpredictive modelingintensive care unitselectronic health records |
| spellingShingle | Chih-Chou Chiu Chung-Min Wu Te-Nien Chien Ling-Jing Kao Chengcheng Li Han-Ling Jiang Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure Journal of Clinical Medicine heart failure machine learning stacking predictive modeling intensive care units electronic health records |
| title | Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure |
| title_full | Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure |
| title_fullStr | Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure |
| title_full_unstemmed | Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure |
| title_short | Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure |
| title_sort | applying an improved stacking ensemble model to predict the mortality of icu patients with heart failure |
| topic | heart failure machine learning stacking predictive modeling intensive care units electronic health records |
| url | https://www.mdpi.com/2077-0383/11/21/6460 |
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