DU-ResNet to Predict Survival in Patient of Heart Failure

Abstract

Abstract Predicting survival in patients with heart disease clinically is a challenging task. Predicting the survival state is very important among patients with heart failure. In this paper, we propose a prediction model for the survival of heart failure patients based on deep learning combined with clinical data of heart failure patients. The proposed model is named DU-ResNet which is designed by combining ResUNet with ResNet50. Most clinical data of patients with heart failure are only numerical heart failure datasets. If one-dimensional clinical data can be converted into two-dimensional image data, the advantages of a deep convolutional neural network in extracting spatial features can be fully realized. For this reason, in this paper, the clinical data of all original patients with heart failure were normalized first, and then, each normalized clinical data point was placed in a certain area of the grid image. Therefore, according to the value of each clinical data point, a gray image with different brightness regions was constructed. After data enhancement was performed on the constructed image dataset of clinical data of heart failure patients to expand the number of samples, DU-ResNet is used to binary classify the expanded dataset, and ten-fold cross-validation and ablation experiments are performed on the dataset. Then, ten-fold cross-validation was used to verify the performance of the proposed DU-ResNet model. The results show that the proposed DU-ResNet model has the best result with the use of four features, with the accuracy was 96.47%, the Specificity was 97.22%, the Sensitivity was 95.71%, the Precision was 96.87%, the F1-score was 96.27% and the MCC was 92.97% after ten-fold cross-validation. In addition, the comprehensive performance of the proposed DU-ResNet model for predicting the survival of patients with heart failure is better than several typical deep learning methods.