Comparing different machine learning techniques for predicting COVID-19 severity.

ABSTRACT:

Background

Coronavirus disease 2019 (COVID-19) is still ongoing spreading globally, machine learning techniques were used in disease diagnosis and to predict treatment outcomes, which showed favorable performance. The present study aims to predict COVID-19 severity at admission by different machine learning techniques including random forest (RF), support vector machine (SVM), and logistic regression (LR). Feature importance to COVID-19 severity were further identified.

Methods

A retrospective design was adopted in the JinYinTan Hospital from January 26 to March 28, 2020, eighty-six demographic, clinical, and laboratory features were selected with LassoCV method, Spearman's rank correlation, experts' opinions, and literature evaluation. RF, SVM, and LR were performed to pred

SUBMITTER: Xiong Y 

PROVIDER: S-EPMC8851750 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature