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

• Published in: American journal of clinical pathology Vol. 157; no. 5; pp. 758 - 766
• Main Authors: Çubukçu, Hikmet Can, Topcu, Deniz İlhan, Bayraktar, Nilüfer, Gülşen, Murat, Sarı, Nuran, Arslan, Ayşe Hande
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 04-05-2022
• Subjects: Accuracy; Algorithms; Artificial intelligence; Coronaviruses; COVID-19; COVID-19 - diagnosis; Datasets; Health aspects; Humans; Laboratories; Learning algorithms; Logistic Models; Machine Learning; Original; Reverse transcription; SARS-CoV-2; Severe acute respiratory syndrome; Severe acute respiratory syndrome coronavirus 2; Support vector machines; Brazil; China; COVID-19; Laboratory tests; SARS-CoV-2; Artificial intelligence; Machine learning
• ISSN: 0002-9173; 1943-7722
• DOI: 10.1093/ajcp/aqab187

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.