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 an...

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Vydáno v:Information sciences Ročník 637; s. 118932
Hlavní autoři: Zhou, Chunjie, Hou, Aihua, Dai, Pengfei, Li, Ali, Zhang, Zhenxing, Mu, Yuejun, Liu, Li
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.08.2023
Témata:
Anomaly detection
Deep learning
Heart failure
Prediction
Risk factors
Heart failure
Deep learning
Risk factors
Anomaly detection
Prediction
ISSN:0020-0255, 1872-6291
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Shrnutí: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%). •Propose the risk factor selection method to get more relevant and valuable data for heart failure prediction.•Present an anomaly detection method to eliminate abnormal data.•Propose an ensemble deep learning model for heart failure prediction.•Evaluate our methods based on our real research project HeartCarer and obtain a higher accuracy of 98.5%.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2023.04.011