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Development of Machine-Learning Model to Predict COVID-19 Mortality: Application of Ensemble Model and Regarding Feature Impacts
- Title
- Development of Machine-Learning Model to Predict COVID-19 Mortality: Application of Ensemble Model and Regarding Feature Impacts
- Authors
- Baik, Seung-Min; Lee, Miae; Hong, Kyung-Sook; Park, Dong-Jin
- Ewha Authors
- 이미애; 홍경숙; 박동진; 백승민
- SCOPUS Author ID
- 이미애; 홍경숙; 박동진; 백승민
- Issue Date
- 2022
- Journal Title
- DIAGNOSTICS
- ISSN
- 2075-4418
- Citation
- DIAGNOSTICS vol. 12, no. 6
- Keywords
- COVID-19; mortality; artificial intelligence; ensemble model
- Publisher
- MDPI
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- This study was designed to develop machine-learning models to predict COVID-19 mortality and identify its key features based on clinical characteristics and laboratory tests. For this, deep-learning (DL) and machine-learning (ML) models were developed using receiver operating characteristic (ROC) area under the curve (AUC) and F1 score optimization of 87 parameters. Of the two, the DL model exhibited better performance (AUC 0.8721, accuracy 0.84, and F1 score 0.76). However, we also blended DL with ML, and the ensemble model performed the best (AUC 0.8811, accuracy 0.85, and F1 score 0.77). The DL model is generally unable to extract feature importance; however, we succeeded by using the Shapley Additive exPlanations method for each model. This study demonstrated both the applicability of DL and ML models for classifying COVID-19 mortality using hospital-structured data and that the ensemble model had the best predictive ability.
- DOI
- 10.3390/diagnostics12061464
- Appears in Collections:
- 의과대학 > 의학과 > Journal papers
- Files in This Item:
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diagnostics-12-01464-v2.pdf(27.13 MB)
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