Development of Machine‐Learning Model to Predict COVID‐19 Mortality: Application of Ensemble Model and Regarding Feature Impacts
- 주제(키워드) artificial intelligence , COVID‐19 , ensemble model , mortality
- 등재 SCIE, SCOPUS
- OA유형 Green Published, gold
- 발행기관 MDPI
- 발행년도 2022
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000193284
- 본문언어 영어
- Published As https://doi.org/10.3390/diagnostics12061464
- PubMed https://pubmed.ncbi.nlm.nih.gov/35741274
초록/요약
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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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