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Related Experiment Video

Updated: Aug 15, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2.

Elias Dritsas¹, Maria Trigka¹

¹Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This summary is machine-generated.

This study used machine learning to identify early symptoms of the SARS-CoV-2 virus, the cause of COVID-19. The Stacking ensemble model achieved over 90% accuracy in detecting early-stage coronavirus disease symptoms.

Keywords:

SARS-CoV-2 data analysis healthcare machine learning prediction

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Area of Science:

Virology
Infectious Diseases
Machine Learning Applications

Background:

The COVID-19 pandemic, caused by the novel SARS-CoV-2 virus, emerged in late 2019.
Severe cases can lead to pneumonia, ARDS, organ failure, and death.
Vaccines, antivirals, and treatments are crucial in managing the disease.

Purpose of the Study:

To develop and evaluate supervised Machine Learning models for early detection of SARS-CoV-2 infection.
To identify key early-stage symptoms indicative of coronavirus disease.

Main Methods:

Experimentation with various supervised Machine Learning models.
Utilizing a dataset of early-stage symptoms for model training and validation.
Employing the Stacking ensemble method for enhanced predictive performance.

Main Results:

The Stacking ensemble model demonstrated superior performance compared to other tested ML models.
Achieved high performance metrics: 90.9% Accuracy, Precision, Recall, and F-Measure.
Attained an Area Under the Curve (AUC) of 96.4%, indicating strong discriminative ability.

Conclusions:

Supervised Machine Learning, particularly the Stacking ensemble, is effective for early SARS-CoV-2 symptom detection.
The developed model shows significant potential for aiding in the early identification of COVID-19.
Accurate early detection can facilitate timely intervention and disease management.