A descriptive study of random forest algorithm for predicting COVID-19 patients outcome.

ABSTRACT:

Background

The outbreak of coronavirus disease 2019 (COVID-19) that occurred in Wuhan, China, has become a global public health threat. It is necessary to identify indicators that can be used as optimal predictors for clinical outcomes of COVID-19 patients.

Methods

The clinical information from 126 patients diagnosed with COVID-19 were collected from Wuhan Fourth Hospital. Specific clinical characteristics, laboratory findings, treatments and clinical outcomes were analyzed from patients hospitalized for treatment from 1 February to 15 March 2020, and subsequently died or were discharged. A random forest (RF) algorithm was used to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients' clinical prognoses.

Results

Seven o

SUBMITTER: Wang J 

PROVIDER: S-EPMC7486830 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature