# COVID-19 diagnosis by routine blood tests using machine learning

arXiv: https://arxiv.org/abs/2006.03476 · HTML: https://mlsanity.com/paper/2006.03476/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [Development of a Risk-Free COVID-19 Screening Algorithm from Routine Blood Tests Using Ensemble Machine Learning](https://mlsanity.com/paper/2108.05660/) (2021)
- [Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network](https://mlsanity.com/paper/2205.09974/) (2022)
- [Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application](https://mlsanity.com/paper/2209.03522/) (2022)
- [Differentiating Viral and Bacterial Infections: A Machine Learning Model Based on Routine Blood Test Values](https://mlsanity.com/paper/2305.07877/) (2023)
- [A Multi-Task Learning Framework for COVID-19 Monitoring and Prediction of PPE Demand in Community Health Centres](https://mlsanity.com/paper/2108.09402/) (2021)
- [Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases](https://mlsanity.com/paper/2206.07365/) (2022)
- [Detection of Risk Predictors of COVID-19 Mortality with Classifier Machine Learning Models Operated with Routine Laboratory Biomarkers](https://mlsanity.com/paper/2210.12342/) (2022)
- [Artificial Intelligence for Infectious Disease Prediction and Prevention: A Comprehensive Review](https://mlsanity.com/paper/2411.10486/) (2024)

## Abstract (arXiv)

Physicians taking care of patients with coronavirus disease (COVID-19) have described different changes in routine blood parameters. However, these changes, hinder them from performing COVID-19 diagnosis. We constructed a machine learning predictive model for COVID-19 diagnosis. The model was based and cross-validated on the routine blood tests of 5,333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected operational ROC point at a sensitivity of 81.9% and specificity of 97.9%. The cross-validated area under the curve (AUC) was 0.97. The five most useful routine blood parameters for COVID19 diagnosis according to the feature importance scoring of the XGBoost algorithm were MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. tSNE visualization showed that the blood parameters of the patients with severe COVID-19 course are more like the parameters of bacterial than viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results present a significant contribution to improvements in COVID-19 diagnosis.