Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6902220 | Procedia Computer Science | 2017 | 8 Pages |
Abstract
Type-2 Diabetes is one of the foremost causes for the increase in mortality across the world-wide. In this context, classification systems help doctors by analyzing the disease data. Radial Basis Function Neural Networks (RBFNN) are extensively used as classifier in medical domain because of its non-iterative nature. The size of the RBFNNs hidden-layer increases on par with dataset size. It's difficult to determining the optimal number of neurons in hidden-layer by cost effectively. In this paper, to address this problem, we have proposed Bat-based clustering algorithm. The proposed method experimented on Pima Indians Diabetes dataset and results outperform the competing approaches.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Damodar Reddy Edla, Ramalingaswamy Cheruku,