Key result
The Convolution Neural Network and Gate Recurrent Unit (CNN GRU) technique achieved 94.5% accuracy in predicting cardiac disease compared to other machine learning algorithms.
Why the study?
Cardiac diseases are a leading cause of death, motivating the development of optimal machine learning approaches to achieve high accuracy in cardiac disease prediction.
Does the CNN GRU technique improve accuracy in predicting cardiac disease compared to other machine learning algorithms?
Comparison
CNN GRU technique vs several machine learning algorithms
Authors
Loading...
May support ML cardiac prediction research; leaves open clinical adoption pending prospective validation.
Does the CNN GRU technique improve accuracy in predicting cardiac disease compared to other machine learning algorithms?
A novel CNN GRU hybrid machine learning technique achieved 94.5% accuracy in predicting cardiac disease.
Ali et al. (2020) studied Cardiac diseases. Convolution Neural Network and Gate Recurrent Unit (CNN GRU) vs. Other machine learning algorithms was evaluated on Accuracy in the prediction of cardiac disease. The Convolution Neural Network and Gate Recurrent Unit (CNN GRU) technique achieved 94.5% accuracy in predicting cardiac disease compared to other machine learning algorithms.
Synapse has enriched 2 closely related papers on similar clinical questions. Consider them for comparative context: