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In this paper we propose a novelty detection approach based on radial basis function neural networks (RBFNN), Markov chains and entropy. We employed it to model novel states and temporal relationships of health datasets.We conduct experiments with one synthetic dataset and three real-world ones available at the UCI repository. For every experiment we present accuracy, precision, recall, specificity,...
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