Combining Supervised and Unsupervised Fuzzy Learning Algorithms for Robust Diabetes Diagnosis

• Published in: Applied sciences Vol. 13; no. 1; p. 351
• Main Authors: Kim, Kwang Baek, Park, Hyun Jun, Song, Doo Heon
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Algorithms; Bias; Classification; Clustering; Datasets; Diabetes; Distance learning; fuzzy c-means; fuzzy max–min neural network; Fuzzy sets; Machine learning; Neural networks; supervised learning; Support vector machines; Traffic flow; unsupervised learning
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13010351

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.