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A tree-based explainable AI model for early detection of Covid-19 using physiological data.

Manar Abu Talib¹, Yaman Afadar², Qassim Nasir²

¹Department of Computer Science, College of Computing and Informatics, University of Sharjah, P.O. Box 27272, Sharjah, UAE. mtalib@sharjah.ac.ae.

BMC Medical Informatics and Decision Making

|June 24, 2024

Summary

This summary is machine-generated.

Wearable devices can predict COVID-19 before symptoms appear. Machine learning models analyzed heart rate and step data, achieving 85% accuracy in early disease detection.

Keywords:

Boosting Classification Deep neural network Interpretability Physiological data XAI

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Area of Science:

Artificial Intelligence
Data Science
Wearable Technology

Background:

The COVID-19 pandemic highlighted the need for early disease detection.
Wearable devices offer valuable physiological data (e.g., heart rate, sleep quality) for identifying inflammatory diseases.
Early detection of COVID-19 is crucial for mitigating its impact.

Purpose of the Study:

To predict the probability of COVID-19 infection before symptom onset using physiological data from wearable devices.
To compare the performance of Gradient Boosting, CatBoost, and TabNet classifiers for COVID-19 detection.
To enhance model interpretability and validate findings on a private dataset.

Main Methods:

Utilized an existing dataset with step counts and heart rate data.
Trained and compared Gradient Boosting, CatBoost, and TabNet models.
Applied an interpretability layer to the best-performing model.
Created and analyzed a private dataset from Fitbit devices.

Main Results:

The CatBoost classifier achieved 85% accuracy on the public dataset, outperforming previous studies.
The pre-trained CatBoost model achieved 81% accuracy on the private Fitbit dataset.
Model interpretability provided detailed assessment of prediction effectiveness.

Conclusions:

Machine learning models, particularly CatBoost, can effectively predict COVID-19 using wearable device data before symptom onset.
The study demonstrates the reliability and generalizability of the models across different datasets.
This approach offers a promising tool for early COVID-19 detection and public health management.