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High performance COVID-19 screening using machine learning
Youssef Zied Elhechmi1, Mehdi Mrad2, Mariem Gdoura3
1Hope Horizon International.
La Tunisie Medicale
|January 15, 2025
Summary
A new machine learning model (MLM) accurately predicts COVID-19 with 97.06% accuracy, offering a rapid and dynamic screening tool for future pandemics. This approach combines clinical features and geographic prevalence for reliable disease detection.
Area of Science:
- Epidemiology
- Medical Informatics
- Machine Learning
Background:
- The COVID-19 pandemic necessitated rapid and reliable screening tools.
- Traditional methods like RT-PCR are time-consuming, and serological tests lack sensitivity.
- Machine learning models offer potential for improved diagnostic accuracy.
Purpose of the Study:
- To develop a machine learning model for rapid and accurate COVID-19 screening.
- To address the limitations of existing diagnostic methods during the pandemic.
- To introduce a dynamic screening approach based on geographic prevalence.
Main Methods:
- A gradient boosting machine learning model (MLM) was developed.
- The MLM incorporated clinical features and daily geographic prevalence of COVID-19.
- The model was trained on 1554 cases and tested on 547 cases.
Main Results:
- The MLM achieved a 97.06% accuracy in predicting RT-PCR positivity for COVID-19.
- The model demonstrated variable sensitivity and specificity based on geographic disease prevalence.
- This introduced the concept of 'dynamic' disease screening.
Conclusions:
- The developed MLM serves as a rapid, reliable, and dynamic screening tool for contagious diseases.
- This approach is particularly valuable for developing countries facing pandemic emergencies.
- Machine learning offers a promising strategy for future public health crisis management.
