Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators?

ABSTRACT:

Objective

This study aimed to implement and evaluate machine learning based-models to predict COVID-19' diagnosis and disease severity.

Methods

COVID-19 test samples (positive or negative results) from patients who attended a single hospital were evaluated. Patients diagnosed with COVID-19 were categorised according to the severity of the disease. Data were submitted to exploratory analysis (principal component analysis, PCA) to detect outlier samples, recognise patterns, and identify important variables. Based on patients' laboratory tests results, machine learning models were implemented to predict disease positivity and severity. Artificial neural networks (ANN), decision trees (DT), partial least squares discriminant analysis (PLS-DA), and K nearest neighbour algorithm (

SUBMITTER: Cobre AF 

PROVIDER: S-EPMC8164361 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature