Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests.

ABSTRACT:

Objectives

The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results.

Methods

We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription-polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and ex

SUBMITTER: Cubukcu HC 

PROVIDER: S-EPMC8690000 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature