Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis.

ABSTRACT: Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of "black box" models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps

SUBMITTER: Storas AM 

PROVIDER: S-EPMC10378376 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature