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.
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