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Background: Predictive modeling is a key component of solutions to manyhealthcare problems. Among all predictive modeling approaches, machine learningmethods often achieve the highest prediction accuracy, but suffer from along-standing open problem precluding their widespread use in healthcare. Mostmachine learning models give no explanation for their prediction results,whereas interpretability is essential for a predictive model to be adopted intypical healthcare settings. Methods: This paper presents the first completemethod for automatically explaining results for any machine learning predictivemodel without degrading accuracy. We did a computer coding implementation ofthe method. Using the electronic medical record data set from the PracticeFusion diabetes classification competition containing patient records from all50 states in the United States, we demonstrated the method on predicting type 2diabetes diagnosis within the next year. Results: For the champion machinelearning model of the competition, our method explained prediction results for87.4% of patients who were correctly predicted by the model to have type 2diabetes diagnosis within the next year. Conclusions: Our demonstration showedthe feasibility of automatically explaining results for any machine learningpredictive model without degrading accuracy.

Automatically Explaining Machine Learning Prediction Results: A Demonstration on Type 2 Diabetes Risk Prediction