A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions
This work addresses the problem of improving evidence-based medical decision-making for clinicians and researchers by providing tools to compare and evaluate machine learning models, though it is incremental as it builds on existing visual analytics approaches.
The paper tackles the challenge of comparing and evaluating multiple machine learning models on clinical data by developing a visual analytics system that reveals models' prediction criteria and consistency, demonstrating its effectiveness in a case study with a publicly available clinical dataset.
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.