An Evolutionary Self-organizing Cost-Sensitive Radial Basis Function Neural Network to Deal with Imbalanced Data in Medical Diagnosis

Class imbalance is a common issue in medical diagnosis. Although standard radial basis function neural network (RBF-NN) has achieved remarkably high performance on balanced data, its ability to classify imbalanced data is still limited. So far as we know, cost-sensitive learning is an advanced imbalanced data processing method. However, few studies have focused on the combination of RBF-NN and cost sensitivity. From our knowledge, only one paper has proposed a cost-sensitive RBF-NN for software defect prediction. However, the authors implemented a fixed RBF-NN structure. In this paper, a novel cost-sensitive RBF-NN that optimizes structure and parameters simultaneously is proposed to handle medical imbalanced data. Genetic algorithm (GA) and improved particle swarm optimization (IPSO) are used to optimize the structure and parameters of cost-sensitive RBF-NN respectively, and the optimization of cost-sensitive RBF-NN based on dynamic structure is realized. A cost-sensitive function determined adaptively by the sample distribution as the objective function of RBF-NN, so that it can adapt to datasets with different sample distributions. Experimental results show that the proposed cost-sensitive RBF-NN outperforms other state-of-the-art representative algorithms for five imbalanced medical diagnostic datasets in term of accuracy and area under curve (AUC). It can improve the accuracy of medical diagnosis and reduce the error rate of medical decisions.