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 external validation data set from Brazil.

Results: The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study's data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%).

Conclusions: ML models presented in this study can be used as clinical decision support tools to contribute to physicians' clinical judgment for COVID-19 diagnoses.

Keywords: Artificial intelligence; COVID-19; Laboratory tests; Machine learning; SARS-CoV-2.

FIGURE 1

Study design. CC, clinical chemistry; LIS, laboratory information system; ML, machine learning; PCR, polymerase chain reaction; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

FIGURE 2

Boruta feature selection plot. Green indicates important features, and blue represents tentative features. ALT, alanine aminotransferase; AST, aspartate aminotransferase; CRP, C-reactive protein; LDH, lactate dehydrogenase; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; RDW, red cell distribution width.

FIGURE 3

Receiver operating characteristic curves of the machine learning (ML) models. A, ML models built using clinical chemistry and CBC parameters. B, ML models built using CBC parameters. AUC, area under the curve; SVM, support vector machine.

FIGURE 4

Receiver operating characteristic curves of the machine learning (ML) models on São Paulo data set. A, ML models built using clinical chemistry and CBC parameters. B, ML models built using CBC parameters. AUC, area under the curve; SVM, support vector machine.