Combining Supervised and Unsupervised Fuzzy Learning Algorithms for Robust Diabetes Diagnosis

In domains that have complex data characteristics and/or noisy data, any single supervised learning algorithm tends to suffer from overfitting. One way to mitigate this problem is to combine unsupervised learning component as a front end of the main supervised learner. In this paper, we propose a hierarchical combination of fuzzy C-means clustering component and fuzzy max–min neural network supervised learner for that purpose. The proposed method is evaluated in a noisy domain (Pima Indian Diabetes open database). The proposed combination showed superior result to standalone fuzzy max–min and backpropagation-based neural network. The proposed method also showed better performance than any single supervised learner tested in the same domain in the literature with high accuracy (80.96%) and was at least competitive in other measures such as sensitivity, specificity, and F1 measure.