# Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task

arXiv: https://arxiv.org/abs/2010.02006 · HTML: https://mlsanity.com/paper/2010.02006/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications](https://mlsanity.com/paper/2108.10451/) (2021)
- [Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review](https://mlsanity.com/paper/2112.12705/) (2021)
- [Pathologist-Like Explanations Unveiled: an Explainable Deep Learning System for White Blood Cell Classification](https://mlsanity.com/paper/2310.13279/) (2023)
- [Review of Interpretable Machine Learning Models for Disease Prognosis](https://mlsanity.com/paper/2405.11672/) (2024)
- [Integrating Explainable AI in Medical Devices: Technical, Clinical and Regulatory Insights and Recommendations](https://mlsanity.com/paper/2505.06620/) (2025)
- [An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient Boosting and Fuzzy Rule-Based Models](https://mlsanity.com/paper/2511.08077/) (2025)

## Abstract (arXiv)

The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis. It is risky to put one's life in the hands of models that medical researchers do not fully understand. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th Jan. 2020 and 5th Mar. 2020, in Zhuhai, China, to identify biomarkers indicative of severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, Partial Dependence Plot (PDP), Individual Conditional Expectation (ICE), Accumulated Local Effects (ALE), Local Interpretable Model-agnostic Explanations (LIME), and Shapley Additive Explanation (SHAP), we identify an increase in N-Terminal pro-Brain Natriuretic Peptide (NTproBNP), C-Reaction Protein (CRP), and lactic dehydrogenase (LDH), a decrease in lymphocyte (LYM) is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at S~ao Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.