COVID-19 mortality prediction using ensemble learning and grey wolf optimization

Lihua Lou; Weidong Xia; Zhen Sun; Shichao Quan; Shaobo Yin; Zhihong Gao; Cai Lin

doi:10.7717/peerj-cs.1209

COVID-19 mortality prediction using ensemble learning and grey wolf optimization

PeerJ Comput Sci. 2023 Feb 24:9:e1209. doi: 10.7717/peerj-cs.1209. eCollection 2023.

Authors

Lihua Lou¹, Weidong Xia¹, Zhen Sun², Shichao Quan², Shaobo Yin¹, Zhihong Gao², Cai Lin¹

Affiliations

¹ Department of Burn, Wound Repair and Regenerative Medicine Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
² Department of Big Data in Health Science, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.

Abstract

COVID-19 is now often moderate and self-recovering, but in a significant proportion of individuals, it is severe and deadly. Determining whether individuals are at high risk for serious disease or death is crucial for making appropriate treatment decisions. We propose a computational method to estimate the mortality risk for patients with COVID-19. To develop the model, 4,711 reported cases confirmed as SARS-CoV-2 infections were used for model development. Our computational method was developed using ensemble learning in combination with a genetic algorithm. The best-performing ensemble model achieves an AUCROC (area under the receiver operating characteristic curve) value of 0.7802. The best ensemble model was developed using only 10 features, which means it requires less medical information so that the diagnostic cost may be reduced while the prognostic time may be improved. The results demonstrate the robustness of the used method as well as the efficiency of the combination of machine learning and genetic algorithms in developing the ensemble model.

Keywords: Artificial intelligence; COVID-19; Data science; Ensemble learning; Genetic algorithm; Grey wolf optimization; Machine learning; Mortality; Prediction.

Associated data

figshare/10.6084/m9.figshare.21739745.v1

Grants and funding

The work by Cai Lin is supported by the Zhejiang Medical and Health Science and Technology Plan Project number 2022PY069. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.