  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.
