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- Title
Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence.
- Authors
Khan, Muhammad Amir; Alsulami, Musleh; Yaqoob, Muhammad Mateen; Alsadie, Deafallah; Saudagar, Abdul Khader Jilani; AlKhathami, Mohammed; Farooq Khattak, Umar
- Abstract
Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces a novel method called the asynchronous federated deep learning approach for cardiac prediction (AFLCP), which combines a heart disease dataset and deep neural networks (DNNs) with an asynchronous learning technique. The proposed approach employs a method for asynchronously updating the parameters of DNNs and incorporates a temporally weighted aggregation technique to enhance the accuracy and convergence of the central model. To evaluate the effectiveness of the proposed AFLCP method, two datasets with various DNN architectures are tested, and the results demonstrate that the AFLCP approach outperforms the baseline method in terms of both communication cost and model accuracy.
- Subjects
ARTIFICIAL neural networks; ARTIFICIAL intelligence; ASYNCHRONOUS learning; CARDIOVASCULAR diseases; DEEP learning
- Publication
Diagnostics (2075-4418), 2023, Vol 13, Issue 14, p2340
- ISSN
2075-4418
- Publication type
Article
- DOI
10.3390/diagnostics13142340