Feature Importance Measures as Explanation for Classification Applied to Hospital Readmission Prediction
Ma. Sheila A. Magboo, Vincent Peter C. Magboo
Abstract
Many machine learning (ML) applications in healthcare that are capable of generating very good performance are still not integrated in the clinical workflow primarily because the end-users, the physicians, could not understand the logic used by these models. For them, these are black boxes incapable of providing reasons for arriving at the diagnosis thus creating trust issues and acceptability. In the end, it is still the physician who will make the final decision and so for the tool to be considered a worthy clinical decision support tool, it should be able to communicate very well how it arrived at the result. Only then can it gain the physician's trust and acceptance. Thus, Explainable AI (XAI) should be feature in any ML application in order to gain acceptability and integration in the workplace. In this study we presented a tool that is capable of predicting patients who will more likely be readmitted and its reasons for arriving at such a conclusion. We used Random Forest (RF), AdaBoost, and K-Nearest Neighbors (K-NN) to build the models, performed hyperparameter tuning in order to improve performance, calculated the feature importance to understand which features are deemed important to each model, and then added a visual explainer using Local Interpretable Model-Agnostic Explanation (LIME) to help the physician understand the logic employed by each model in making the classification.