XGBoost: Interpretable Machine Learning Approach in Medicine

Abstract

This research tries to establish a communication bridge between the decision making of a practicing physician and a machine learning model. Machine learning has long been used to assist in diagnosing patients through deep learning and medical image analysis. However, in medical science, it is possible to have multiple right answers when treating patients. This leaves the practitioners room to exercise their judgement on what would be best for the patient. The machine learning model can be a communication tool to assist the practitioners in reach the final decision. In this paper, the machine learning model used for this experiment is XGBoost, a gradient boosted decision tree. Through the experiment with the real patient data, we show how decision tree constructions are easily readable for medical determination.