Online COVID-19 diagnosis prediction using complete blood count: an innovative tool for public health.

ABSTRACT:

Background

COVID-19, caused by SARS-CoV-2, presents distinct diagnostic challenges due to its wide range of clinical manifestations and the overlapping symptoms with other common respiratory diseases. This study focuses on addressing these difficulties by employing machine learning (ML) methodologies, particularly the XGBoost algorithm, to utilize Complete Blood Count (CBC) parameters for predictive analysis.

Methods

We performed a retrospective study involving 2114 COVID-19 patients treated between December 2022 and January 2023 at our healthcare facility. These patients were classified into fever (1057 patients) and pneumonia groups (1057 patients), based on their clinical symptoms. The CBC data were utilized to create predictive models, with model performance evaluated th

SUBMITTER: Teng X 

PROVIDER: S-EPMC10729447 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature