Mining Outliers from Medical Datasets Using Neighbourhood Rough Set and Data Classification with Neural Network

In this paper, a neighbourhood rough set is modified and applied as a data pre-processing method to select samples from a data set before training with a radial basis function neural network (RBFN). Data samples that are not selected for training is considered as outliers. Four medical datasets from a famous repository were used and results were compared in terms of number of training samples and accuracy between the proposed model and RBFN. The results are encouraging where classification accuracy of the proposed model is improved after outlier removal. Results are compared with other classification models as well using a medical dataset. The proposed model is competitive to give high classification accuracy.