Development of a COVID-19 early risk assessment system based on multiple machine learning algorithms and routine blood tests: a real-world study.

ABSTRACT:

Backgrounds

During the Coronavirus Disease 2019 (COVID-19) epidemic, the massive spread of the disease has placed an enormous burden on the world's healthcare and economy. The early risk assessment system based on a variety of machine learning (ML) algorithms may be able to provide more accurate advice on the classification of COVID-19 patients, offering predictive, preventive, and personalized medicine (PPPM) solutions in the future.

Methods

In this retrospective study, we divided a portion of the data into training and validation cohorts in a 7:3 ratio and established a model based on a combination of two ML algorithms first. Then, we used another portion of the data as an independent testing cohort to determine the most accurate and stable model and compared it with other

SUBMITTER: Qin Q 

PROVIDER: S-EPMC11471604 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature