  Cardiovascular diseases (CVDs) encompass a group of disorders affecting the
heart and blood vessels, including conditions such as coronary artery disease,
heart failure, stroke, and hypertension. In cardiovascular diseases, heart
failure is one of the main causes of death and also long-term suffering in
patients worldwide. Prediction is one of the risk factors that is highly
valuable for treatment and intervention to minimize heart failure. In this
work, an attention learning-based heart failure prediction approach is proposed
on EHR(electronic health record) cardiovascular data such as ejection fraction
and serum creatinine. Moreover, different optimizers with various learning rate
approaches are applied to fine-tune the proposed approach. Serum creatinine and
ejection fraction are the two most important features to predict the patient's
heart failure. The computational result shows that the RMSProp optimizer with
0.001 learning rate has a better prediction based on serum creatinine. On the
other hand, the combination of SGD optimizer with 0.01 learning rate exhibits
optimum performance based on ejection fraction features. Overall, the proposed
attention learning-based approach performs very efficiently in predicting heart
failure compared to the existing state-of-the-art such as LSTM approach.
