Key points are not available for this paper at this time.
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.
Edla et al. (Sun,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: