Discriminant Decision Making of Cardiovascular Diseases Based on Cloud-Based Convolutional Attention Network

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Wei Liu, Congjun Rao
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引用次数: 0

Abstract

Cardiovascular diseases (CVDs) have become the number one killer affecting human health. In order to reduce the burden of medical workers, facilitate government screening of the population and enable patients to conduct their own health status checks, there is an urgent need for a complementary diagnostic system to predict the occurrence of CVD. In this study, a new cloud-based convolutional attention network (C-CAN) model is proposed for the discriminant decision making of CVD. In this model, the indicator data for discriminant decision making of CVD are trained using an improved one-dimensional convolutional neural network (1D CNN) model structure based on the correlation of factors influencing CVD given by decision-making trial and evaluation laboratory (DEMATEL) and cloud models. This 1D CNN model consists of a convolutional pooling module, an attention module and a fully connected module. The cloud model is used to process the original data based on the discriminating opinion of experts, so as to select the important factors that affect CVD. The attention mechanism is effective in augmenting attention to the essential elements of the data and reducing attention to the less important features. Both have similarities in that they are effective in augmenting the important features in the data and combine with each other to achieve better results. Moreover, the C-CAN is compared with decision tree (DT), K-nearest neighbors (KNN), random forests (RF) and normal CNN according to the CVD dataset from the Kaggle platform. The results show that the classification accuracy, precision, recall and F1 value of C-CAN are all higher than that of all compared models. Further, the proposed model is further externally validated using other imbalanced datasets, and the results indicate that C-CAN has good resilience for imbalanced data. Our findings suggest that C-CAN represents a promising new approach that may somehow address the challenges associated with deep learning (DL) in the medical field.

基于云卷积注意力网络的心血管疾病判别决策
心血管疾病(CVD)已成为影响人类健康的头号杀手。为了减轻医务工作者的负担,方便政府对人群进行筛查,并使患者能够自己进行健康状况检查,迫切需要一种辅助诊断系统来预测心血管疾病的发生。本研究提出了一种新的基于云的卷积注意力网络(C-CAN)模型,用于心血管疾病的判别决策。在该模型中,根据决策试验与评估实验室(DEMATEL)和云模型给出的心血管疾病影响因素的相关性,使用改进的一维卷积神经网络(1D CNN)模型结构训练心血管疾病判别决策的指标数据。该一维 CNN 模型由卷积池模块、注意力模块和全连接模块组成。云模型用于根据专家的鉴别意见处理原始数据,从而筛选出影响心血管疾病的重要因素。注意力机制能有效增强对数据基本要素的注意力,减少对不太重要特征的注意力。两者的相似之处在于都能有效地增强数据中的重要特征,并相互结合以达到更好的效果。此外,根据 Kaggle 平台上的 CVD 数据集,将 C-CAN 与决策树(DT)、K-近邻(KNN)、随机森林(RF)和普通 CNN 进行了比较。结果表明,C-CAN 的分类准确率、精确度、召回率和 F1 值均高于所有比较模型。此外,我们还使用其他不平衡数据集对所提出的模型进行了进一步的外部验证,结果表明 C-CAN 对不平衡数据具有良好的适应能力。我们的研究结果表明,C-CAN 是一种很有前途的新方法,可以在某种程度上解决深度学习(DL)在医疗领域面临的挑战。
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来源期刊
CiteScore
7.40
自引率
14.30%
发文量
0
审稿时长
6 months
期刊介绍: International Journal of Information Technology and Decision Making (IJITDM) provides a global forum for exchanging research findings and case studies which bridge the latest information technology and various decision-making techniques. It promotes how information technology improves decision techniques as well as how the development of decision-making tools affects the information technology era. The journal is peer-reviewed and publishes both high-quality academic (theoretical or empirical) and practical papers in the broad ranges of information technology related topics including, but not limited to the following: • Artificial Intelligence and Decision Making • Bio-informatics and Medical Decision Making • Cluster Computing and Performance • Data Mining and Web Mining • Data Warehouse and Applications • Database Performance Evaluation • Decision Making and Distributed Systems • Decision Making and Electronic Transaction and Payment • Decision Making of Internet Companies • Decision Making on Information Security • Decision Models for Electronic Commerce • Decision Models for Internet Based on Companies • Decision Support Systems • Decision Technologies in Information System Design • Digital Library Designs • Economic Decisions and Information Systems • Enterprise Computing and Evaluation • Fuzzy Logic and Internet • Group Decision Making and Software • Habitual Domain and Information Technology • Human Computer Interaction • Information Ethics and Legal Evaluations • Information Overload • Information Policy Making • Information Retrieval Systems • Information Technology and Organizational Behavior • Intelligent Agents Technologies • Intelligent and Fuzzy Information Processing • Internet Service and Training • Knowledge Representation Models • Making Decision through Internet • Multimedia and Decision Making [...]
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