A novel approach for predicting heart failure survival using a rectangular coded network.

This paper provides both effortless augmentation of data and efficient creation of images according to the image input size of deep learning models by converting almost all numerical data into 24-bit images of particular standards. As cardiovascular disease is a cause of mortality, artificial intelligence-based architectures may play an important role here, and predicting survival from heart failure is a great challenge. For this purpose, we adjust the image input size according to different deep learning architectures by using the automated feature engineering technique in the approach, which can be easily used in different numerical datasets. Afterward, we horizontally augment the transpose of the new features obtained, thus creating the 8-bit rectangular coded images. After converting these images to 24-bit, we rotate them into multiples of 15° using different coefficients to augment the dataset. We train the ResNet18 and ResNet50 architectures using these images in the new data. According to the findings, the performance metrics (Accuracy, Sensitivity, Specificity, and F1 Score) are quantified as 0.9331-0.9610-0.8742-0.9512 for ResNet18 and 0.9617-0.9743-0.9349-0.9718 for ResNet50. Evaluated alongside current literature, these findings confirm that the proposed methodology represents an innovative, competitive, and highly applicable solution for heart failure classification. The proposed novel method allows numerical data to be evaluated as image representations and, accordingly, to be easily utilized in image processing, data augmentation, and similar techniques.