Exploring Metabolic Anomalies in COVID-19 and Post-COVID-19: A Machine Learning Approach with Explainable Artificial Intelligence

Overview

This repository hosts the Jupyter Notebooks and datasets used in our study "Exploring Metabolic Anomalies in COVID-19 and Post-COVID-19: A Machine Learning Approach with Explainable Artificial Intelligence". Our research employed machine learning (ML) and explainable artificial intelligence (XAI) to analyze metabolic alterations in COVID-19 and Post-COVID-19 patients.

Machine Learning Multiclass XAI

Machine Learning Biclass, XAI and subgroup Discovery

Control vs COVID-19

Control vs POST-COVID-19

COVID-19 vs POST-COVID-19

How to Use

Click on the "Open in Colab" link next to the notebook you wish to view.
Google Colab will open in your web browser. You may need to sign in with your Google account.
Once in Colab, you can run the notebook cells sequentially to reproduce our analysis or modify the code to perform your analysis.   
NOTE: After installing the different package versions please restart the kernel

License

This project is licensed under the MIT License - see the LICENSE file for details.