Homogeneous ensemble models for predicting infection levels and mortality of COVID-19 patients: Evidence from China.

ABSTRACT:

Background

Persistence of long-term COVID-19 pandemic is putting high pressure on healthcare services worldwide for several years. This article aims to establish models to predict infection levels and mortality of COVID-19 patients in China.

Methods

Machine learning models and deep learning models have been built based on the clinical features of COVID-19 patients. The best models are selected by area under the receiver operating characteristic curve (AUC) scores to construct two homogeneous ensemble models for predicting infection levels and mortality, respectively. The first-hand clinical data of 760 patients are collected from Zhongnan Hospital of Wuhan University between 3 January and 8 March 2020. We preprocess data with cleaning, imputation, and normalization.

Resul

SUBMITTER: Wang J 

PROVIDER: S-EPMC9630904 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature