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

TL;DR

This study employs interpretable machine learning models to identify key biomarkers associated with COVID-19 severity, providing insights that align with medical research and validating findings on large datasets.

Contribution

It introduces a comprehensive interpretability approach to COVID-19 severity prediction, revealing significant biomarkers and validating results across multiple datasets.

Findings

Increased NTproBNP, CRP, and LDH are linked to severe COVID-19 cases.

Decreased lymphocyte levels correlate with higher risk of death.

Leukocytes, eosinophils, and platelets are identified as biomarkers in a large dataset.

Abstract

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.