计算机科学
可扩展性
集成学习
机器学习
人工智能
预警系统
心力衰竭
深度学习
数据挖掘
风险因素
异常检测
医学
数据库
内科学
电信
作者
Chunjie Zhou,Aihua Hou,Pengfei Dai,Ali Li,Zhenxing Zhang,Yuejun Mu,li Liu
标识
DOI:10.1016/j.ins.2023.04.011
摘要
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%).
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