# Development of a Risk-Free COVID-19 Screening Algorithm from Routine Blood Tests Using Ensemble Machine Learning

arXiv: https://arxiv.org/abs/2108.05660 · HTML: https://mlsanity.com/paper/2108.05660/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [COVID-19 diagnosis by routine blood tests using machine learning](https://mlsanity.com/paper/2006.03476/) (2020)
- [Triaging moderate COVID-19 and other viral pneumonias from routine blood tests](https://mlsanity.com/paper/2005.06546/) (2020)

## Built on by (most cited first, 3 of 3)

- [Survival Prediction of Heart Failure Patients using Stacked Ensemble Machine Learning Algorithm](https://mlsanity.com/paper/2108.13367/) (2021)
- [Telehealthcare and Telepathology in Pandemic: A Noninvasive, Low-Cost Micro-Invasive and Multimodal Real-Time Online Application for Early Diagnosis of COVID-19 Infection](https://mlsanity.com/paper/2109.07846/) (2021)
- [Prediction Model for Mortality Analysis of Pregnant Women Affected With COVID-19](https://mlsanity.com/paper/2111.11477/) (2021)

## Abstract (arXiv)

The Reverse Transcription Polymerase Chain Reaction (RTPCR)} test is the silver bullet diagnostic test to discern COVID infection. Rapid antigen detection is a screening test to identify COVID positive patients in little as 15 minutes, but has a lower sensitivity than the PCR tests. Besides having multiple standardized test kits, many people are getting infected and either recovering or dying even before the test due to the shortage and cost of kits, lack of indispensable specialists and labs, time-consuming result compared to bulk population especially in developing and underdeveloped countries. Intrigued by the parametric deviations in immunological and hematological profile of a COVID patient, this research work leveraged the concept of COVID-19 detection by proposing a risk-free and highly accurate Stacked Ensemble Machine Learning model to identify a COVID patient from communally available-widespread-cheap routine blood tests which gives a promising accuracy, precision, recall and F1-score of 100%. Analysis from R-curve also shows the preciseness of the risk-free model to be implemented. The proposed method has the potential for large scale ubiquitous low-cost screening application. This can add an extra layer of protection in keeping the number of infected cases to a minimum and control the pandemic by identifying asymptomatic or pre-symptomatic people early.