Automatically Explaining Machine Learning Prediction Results: A Demonstration on Type 2 Diabetes Risk Prediction
This addresses the need for interpretability in healthcare predictive models, enabling broader adoption, though it is incremental as it builds on existing methods.
The paper tackled the problem of machine learning models lacking interpretability in healthcare by presenting a method to automatically explain prediction results without reducing accuracy, achieving explanations for 87.4% of correctly predicted type 2 diabetes cases.
Background: Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. Methods: This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on predicting type 2 diabetes diagnosis within the next year. Results: For the champion machine learning model of the competition, our method explained prediction results for 87.4% of patients who were correctly predicted by the model to have type 2 diabetes diagnosis within the next year. Conclusions: Our demonstration showed the feasibility of automatically explaining results for any machine learning predictive model without degrading accuracy.