| Title: | Identifying Predictors of COVID-19 Mortality Using Machine Learning |
| Journal: | Life |
| Published: | 6 Apr 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35455038/ |
| DOI: | https://doi.org/10.3390/life12040547 |
| URL: | https://www.mdpi.com/2075-1729/12/4/547/pdf?version=1650270970 |
Publication 7158
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Abstract
(1) Background: Coronavirus disease 2019 (COVID-19) is a dominant, rapidly spreading respiratory disease. However, the factors influencing COVID-19 mortality still have not been confirmed. The pathogenesis of COVID-19 is unknown, and relevant mortality predictors are lacking. This study aimed to investigate COVID-19 mortality in patients with pre-existing health conditions and to examine the association between COVID-19 mortality and other morbidities. (2) Methods: De-identified data from 113,882, including 14,877 COVID-19 patients, were collected from the UK Biobank. Different types of data, such as disease history and lifestyle factors, from the COVID-19 patients, were input into the following three machine learning models: Deep Neural Networks (DNN), Random Forest Classifier (RF), eXtreme Gradient Boosting classifier (XGB) and Support Vector Machine (SVM). The Area under the Curve (AUC) was used to measure the experiment result as a performance metric. (3) Results: Data from 14,876 COVID-19 patients were input into the machine learning model for risk-level mortality prediction, with the predicted risk level ranging from 0 to 1. Of the three models used in the experiment, the RF model achieved the best result, with an AUC value of 0.86 (95% CI 0.84-0.88). (4) Conclusions: A risk-level prediction model for COVID-19 mortality was developed. Age, lifestyle, illness, income, and family disease history were identified as important predictors of COVID-19 mortality. The identified factors were related to COVID-19 mortality.7 Authors
- Tsz-Kin Wan
- Rui-Xuan Huang
- Thomas Wetere Tulu
- Jun-Dong Liu
- Asmir Vodencarevic
- Chi-Wah Wong
- Kei-Hang Katie Chan
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