The ability to predict patterns of Cardiac Arrest with high accuracy can significantly contribute to more effective prevention and treatment. This research focuses on predicting patterns of Cardiac Arrest based on eight vital sign data using Recurrent Neural Network (RNN) methods with LSTM and Bi-LSTM architectures. In order to address the issue of imbalanced data, undersampling techniques and …
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