Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence.

Autor: Khan MA; Department of Computer Science, COMSATS University Islamabad Abbottabad Campus, Abbottabad 22060, Pakistan., Alsulami M; Information Systems Department, Umm Al-Qura University, Makkah 21961, Saudi Arabia., Yaqoob MM; Department of Computer Science, COMSATS University Islamabad Abbottabad Campus, Abbottabad 22060, Pakistan., Alsadie D; Information Systems Department, Umm Al-Qura University, Makkah 21961, Saudi Arabia., Saudagar AKJ; Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia., AlKhathami M; Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia., Farooq Khattak U; School of Information Technology, UNITAR International University, Kelana Jaya, Petaling Jaya 47301, Selangor, Malaysia.
Jazyk: angličtina
Zdroj: Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2023 Jul 11; Vol. 13 (14). Date of Electronic Publication: 2023 Jul 11.
DOI: 10.3390/diagnostics13142340
Abstrakt: 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.
Databáze: MEDLINE
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