Medicine
3 years ago

Revolutionizing ECG Signal Classification with Hyperparameter Tuning Boosting Accuracy to 79%

Algorithms for intelligent systems
Hyperparameter Tuning of Dense Neural Network for ECG Signal Classification
S. Clement Virgeniya, E. Ramaraj

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The article discusses how tuning the settings of a dense neural network can improve its ability to classify ECG signals. By using a method called grid search cross validation, the researchers found that adjusting these settings led to a 79% accuracy rate in classifying the signals. The data for the study was stored and retrieved from MongoDB, a type of database.