  When using machine learning techniques in decision-making processes, the
interpretability of the models is important. In the present paper, we adopted
the Shapley additive explanation (SHAP), which is based on fair profit
allocation among many stakeholders depending on their contribution, for
interpreting a gradient-boosting decision tree model using hospital data. For
better interpretability, we propose two novel techniques as follows: (1) a new
metric of feature importance using SHAP and (2) a technique termed feature
packing, which packs multiple similar features into one grouped feature to
allow an easier understanding of the model without reconstruction of the model.
We then compared the explanation results between the SHAP framework and
existing methods. In addition, we showed how the A/G ratio works as an
important prognostic factor for cerebral infarction using our hospital data and
proposed techniques.
