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Remote Laboratory Management: Respiratory Virus Diagnostics

Remote Laboratory Management: Respiratory Virus Diagnostics

Published on: April 6, 2019

Prediction equations for detecting COVID-19 infection using basic laboratory parameters.

Shirin Dasgupta¹, Shuvankar Das², Debarghya Chakraborty²

¹Dr. B. C. Roy Multi Speciality Medical Research Centre, Indian Institute of Technology Kharagpur, West Bengal, India.

Journal of Family Medicine and Primary Care

|July 29, 2024

Summary

Machine learning models predict COVID-19 infection using five basic parameters, offering a cost-effective alternative to RT-PCR. Artificial Neural Network achieved 97.06% accuracy, identifying C-reactive protein as a key indicator.

Keywords:

Artificial neural network COVID-19 laboratory parameters multivariate adaptive regression splines predictive models

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Last Updated: Jun 18, 2025

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Area of Science:

Medical diagnostics
Computational biology
Infectious disease research

Background:

Coronavirus disease 2019 (COVID-19) became a global pandemic from 2019-2022.
Reverse transcription-polymerase chain reaction (RT-PCR) is the standard for COVID-19 detection but has limitations.
There is a need for accessible and affordable diagnostic methods.

Purpose of the Study:

To develop a cost-effective machine learning (ML) approach for COVID-19 detection.
To utilize five basic clinical parameters as predictors.
To offer an alternative to RT-PCR.

Main Methods:

Two ML models, Artificial Neural Network (ANN) and Multivariate Adaptive Regression Splines (MARS), were developed.
The models used five parameters: age, total leucocyte count, red blood cell count, platelet count, and C-reactive protein (CRP).
Data from 171 patients with suspected COVID-19 symptoms were analyzed.

Main Results:

ANN achieved 97.06% accuracy and MARS achieved 91.18% accuracy in predicting COVID-19.
C-reactive protein (CRP) was identified as the most significant predictive parameter.
Predictive mathematical equations for both models were generated.

Conclusions:

The proposed ML models provide a simple and effective method for COVID-19 detection.
These models can assist medical practitioners in diagnosing COVID-19 using basic parameters.
The study offers a valuable, low-cost diagnostic tool.