aceA 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 archit…
Converting numerical clinical data into 24-bit rectangular coded images and training ResNet architectures improved heart failure survival prediction, reaching reported accuracy up to 0.9617.
Evidence
- Peer-reviewedPhysical and Engineering Sciences in Medicine2026-08-15
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Truvace Impact Record TRV-2026-0797, v1: “A novel approach for predicting heart failure survival using a rectangular coded network.” Truvace, 2026-08-17. /record/TRV-2026-0797 (accessed at citation time). sha256 01248c55644bcdf8…
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