  There is a growing trend of applying machine learning methods to medical
datasets in order to predict patients' future status. Although some of these
methods achieve high performance, challenges still exist in comparing and
evaluating different models through their interpretable information. Such
analytics can help clinicians improve evidence-based medical decision making.
In this work, we develop a visual analytics system that compares multiple
models' prediction criteria and evaluates their consistency. With our system,
users can generate knowledge on different models' inner criteria and how
confidently we can rely on each model's prediction for a certain patient.
Through a case study of a publicly available clinical dataset, we demonstrate
the effectiveness of our visual analytics system to assist clinicians and
researchers in comparing and quantitatively evaluating different machine
learning methods.
