Risk factor refinement and ensemble deep learning methods on prediction of heart failure using real healthcare records

The prediction of heart failure (HF) is crucial in preventing disease progression by implementing lifestyle changes and pharmacological interventions before the onset of heart diseases. While there have been numerous attempts to predict HF, many have failed to consider the coexisting risk factors and their complex relationships with one another. In this research paper, we present an early warning and prediction method for HF using deep learning approaches. Our proposed method involves a risk factor selection method to identify significant risk factors that contain relevant and valuable information for HF prediction. Additionally, we present an anomaly detection method to eliminate abnormal data that may be caused by mood changes or environmental factors. Finally, we propose an ensemble deep learning model for HF prediction based on scalable conjugate-gradient concept and back propagation learning algorithm that aims to predict and provide early warning of HF in massive medical data. We evaluate our proposed method based on our real research project, HeartCarer, and achieve an accuracy of 98.5%, which surpasses other state-of-the-art methods and our prior work (90%).