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

Jie WangHeping YuQingquan HuaShuili JingZhifen LiuXiang PengCheng'an CaoYongwen Luo

Published in: PeerJ (2020)

We applied an RF algorithm to predict the mortality of COVID-19 patients with high accuracy and identified LDH higher than 500 U/L and Myo higher than 80 ng/ml to be potential risk factors for the prognoses of COVID-19 patients in the early stage of the disease.

Keyphrases

sars cov
early stage
machine learning
deep learning
neural network
climate change
cardiovascular events
sentinel lymph node
lymph node