A deep neural network with modified random forest incremental interpretation approach for diagnosing diabetes in smart healthcare

Artificial intelligence (AI) applications based on deep learning for diagnosing type-II diabetes are sometimes difficult to understand and communicate even as patients are eager to understand the rationale behind the diagnostic results. Accordingly, recent studies have used multiple simple rules to adequately explain the diagnostic process and results to patients. However, this can cause patient confusion as the rules vary. Hence, this study proposes a deep neural network (DNN) with random forest (RF) and modified random forest incremental interpretation (MRFII) approach for diagnosing diabetes. This method first entails constructing a DNN to predict the probability of a patient having diabetes. To make the prediction result explainable, an RF is built to explain the process and results in terms of multiple simple decision rules. Additionally, to eliminate patient confusion, the MRFII is proposed to sort and aggregate the decision rules for a specific patient. A certainty mechanism is also established to feed back the explanation results from RF to improve the effectiveness of the DNN. The proposed method was applied to a diabetes dataset from the National Institute of Diabetes and Digestive and Kidney Diseases, and the results showed that this approach provided a more concise and accurate explanation than existing explainable artificial intelligence (XAI) techniques for the same purpose.