A CNN-based novel solution for determining the survival status of heart failure patients with clinical record data: numeric to image

Abstract

The aim of this study is to effectively evaluate numerical data, which are frequently encountered in the medical field, with popular deep learning-based Convolutional Neural Network (CNN) models. Heart failure is a common disease worldwide and it is very important to identify patients with a high survival rate and whose condition will deteriorate. A heart failure dataset consisting of numerical values only, needs to be converted into image data for analysis using the advantages of CNN. For this, first all raw data are normalized, then each normalized feature is placed in a region in the grid image. Thus, images with different brightness regions are obtained according to the numerical value of each feature. After the data augmentation step, these images are trained with five different CNN models (GoogleNet, MobileNet v2, ResNet18, ResNet50 and ResNet101) and classified. The highest accuracy of 95.13 % is obtained with the ResNet18 model and this accuracy is superior to studies using previous numerical raw data. The success proves the applicability of the proposed method and shows that numerical data in different fields can be easily classified with CNN models.