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- W4387379966 abstract "Abstract Interatrial septal aneurysms are complex cardiovascular conditions that demand accurate and reliable diagnostic tools. In this study, we present a comparative analysis of two distinct machine learning approaches for the detection of interatrial septal aneurysms using electrocardiogram (ECG) data and images. The first approach employs an Artificial Neural Network (ANN), while the second leverages Convolutional Neural Networks (CNN). Our results reveal a significant disparity in performance between these two methods. The CNN-based model achieves an impressive accuracy of 98%, surpassing the ANN's accuracy of 70%. Similarly, the F1 score, a measure of model precision and recall, demonstrates superior performance for the CNN (93%) compared to the ANN (73%). Moreover, the area under the Receiver Operating Characteristic (ROC) curve highlights the CNN's robustness with an AUC of 0.9, while the ANN lags behind with an AUC of 0.789. This study underscores the effectiveness of CNNs in accurately identifying interatrial septal aneurysms from ECG images, providing valuable insights for the development of advanced diagnostic tools in cardiovascular medicine. These findings emphasize the potential of deep learning techniques to enhance the accuracy and efficiency of disease detection in clinical settings." @default.
- W4387379966 created "2023-10-06" @default.
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- W4387379966 date "2023-10-05" @default.
- W4387379966 modified "2023-10-16" @default.
- W4387379966 title "Comparative Analysis of Interatrial Septal Aneurysm Detection: ECG Image-Based CNN vs. ECG Data- Driven ANN Approach" @default.
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- W4387379966 doi "https://doi.org/10.21203/rs.3.rs-3380465/v1" @default.
- W4387379966 hasPublicationYear "2023" @default.
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