Flexible and objective diagnosis of type II diabetes by using a fuzzy deep learning ensemble approach

Abstract

Deep learning (DL) applications have potential for improving the accuracy of type II diabetes diagnoses. However, existing DL applications for the diagnosis of type II diabetes have several drawbacks. For example, they maximize overall diagnostic performance rather than the diagnostic performance for each patient, they do not use objective rules to identify whether a patient has type II diabetes, and they sometimes provide the same diagnostic results for patients with different real diagnoses. To address these drawbacks, the present study developed a fuzzy DL ensemble (FDLE) approach. In this approach, several autoencoder (AE)–fuzzy deep neural networks (FDNNs) with different configurations are constructed and used to predict the probability of a patient having type II diabetes. The probability predictions are fuzzy values based on the patient’s attributes. The fuzzy probabilities predicted by the constructed AE-FDNNs are then aggregated using the fuzzy weighted intersection–radial basis function method. Subsequently, on the basis of the aggregated result, several objective and subjective diagnostic rules are created. The developed FDLE approach was applied to a real case to examine its effectiveness. According to the experimental results, this approach outperformed 10 existing methods by up to 21% in terms of accuracy in diagnosing type II diabetes. The different diagnostic rules created in the FDLE approach complement each other and facilitate an accurate diagnosis.