Selector: PSO as Model Selector for Dual-Stage Diabetes Network

Abstract Diabetes is a chronic disease caused by insulin deficiency, and it should be detected in the early stages for effective treatment. In this paper, the Diabetes-Network (Dia-Net) is proposed to increase diabetes predictive accuracy. The proposed Dia-Net is a dual-stage network. It combines both optimized probabilistic neural network (OPNN) and optimized radial basis function neural network (ORBFNN) in the first stage. Hence, Dia-Net possesses the advantages of both the models. In the second stage, the linear support vector machine is used. As the dataset size increases, both RBFNN and PNN perform better, but both suffers from complexity and computational problems. To address these problems, in this paper, particle swarm optimization-based clustering is employed for discovering centers in high-dense regions. This reduces the size of the hidden layer of both RBFNN and PNNs. Experiments are carried out on the Pima Indians Diabetes dataset. The Experimental results showed that the proposed Dia-Net model outperformed individual as well as state-of-the-art models.

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