A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion
A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion
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DOI：
10.1016/j.inffus.2020.06.008
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发表时间：
2020-11-01
影响因子：
18.6
通讯作者：
Kwak, Kyung-Sup
中科院分区：
文献类型：
--
作者：
Ali, Farman;El-Sappagh, Shaker;Kwak, Kyung-Sup
准确预测心脏病对于在心脏病发作之前有效治疗心脏病患者至关重要。这一目标可以通过使用具有丰富的心脏病医疗数据的最佳机器学习模型来实现。最近已经提出了各种基于机器学习的系统来预测和诊断心脏病。然而，由于缺乏可以使用不同数据源进行心脏病预测的智能框架，这些系统无法处理高维数据集。此外，现有的系统利用传统技术从数据集中选择特征，并基于特征的重要性计算特征的一般权重。这些方法也未能提高心脏病诊断的性能。本文提出了一种使用集成深度学习和特征融合方法进行心脏病预测的智能医疗系统。首先，特征融合方法将从传感器数据和电子医疗记录中提取的特征相结合，以生成有价值的医疗保健数据。其次，信息增益技术消除了不相关和冗余的功能，并选择重要的，这减少了计算负担，提高了系统的性能。此外，条件概率方法为每个类别计算特定的特征权重，这进一步提高了系统性能。最后，集成深度学习模型被训练用于心脏病预测。所提出的系统与心脏病数据进行评估，并与传统的基于特征融合，特征选择和加权技术的分类器进行比较。所提出的系统获得98.5%的准确性，这是高于现有的系统。这一结果表明，与其他最先进的方法相比，我们的系统对心脏病的预测更有效。
The accurate prediction of heart disease is essential to efficiently treating cardiac patients before a heart attack occurs. This goal can be achieved using an optimal machine learning model with rich healthcare data on heart diseases. Various systems based on machine learning have been presented recently to predict and diagnose heart disease. However, these systems cannot handle high-dimensional datasets due to the lack of a smart framework that can use different sources of data for heart disease prediction. In addition, the existing systems utilize conventional techniques to select features from a dataset and compute a general weight for them based on their significance. These methods have also failed to enhance the performance of heart disease diagnosis. In this paper, a smart healthcare system is proposed for heart disease prediction using ensemble deep learning and feature fusion approaches. First, the feature fusion method combines the extracted features from both sensor data and electronic medical records to generate valuable healthcare data. Second, the information gain technique eliminates irrelevant and redundant features, and selects the important ones, which decreases the computational burden and enhances the system performance. In addition, the conditional probability approach computes a specific feature weight for each class, which further improves system performance. Finally, the ensemble deep learning model is trained for heart disease prediction. The proposed system is evaluated with heart disease data and compared with traditional classifiers based on feature fusion, feature selection, and weighting techniques. The proposed system obtains accuracy of 98.5%, which is higher than existing systems. This result shows that our system is more effective for the prediction of heart disease, in comparison to other state-of-the-art methods.