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The low proportion and the rapid evolvement of major adverse cardiac events (MACE) present challenges for predicting MACE by machine learning models. In this paper, we propose a method to predict MACE from large-scale imbalanced EMR data by using a network-based one-class classifier. It only used the reliably known MACE samples to establish the hyperspherical model. Experiments show that our model outperforms the state-of-the-art models.
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