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

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), [Formula: see text]-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.