Predicting Heart Failure with Attention Learning Techniques Utilizing Cardiovascular Data

TL;DR

This paper introduces an attention learning-based method for predicting heart failure using electronic health record data, demonstrating superior performance over existing models by fine-tuning optimizers and learning rates.

Contribution

It proposes a novel attention learning approach for heart failure prediction utilizing cardiovascular data, with optimized training strategies for improved accuracy.

Findings

RMSProp with 0.001 learning rate excels with serum creatinine.

SGD with 0.01 learning rate performs best with ejection fraction.

The proposed method outperforms existing models like LSTM in prediction accuracy.

Abstract

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.