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Article

Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence

by
Muhammad Amir Khan
1,
Musleh Alsulami
2,*,
Muhammad Mateen Yaqoob
1,
Deafallah Alsadie
2,
Abdul Khader Jilani Saudagar
3,
Mohammed AlKhathami
3 and
Umar Farooq Khattak
4,*
1
Department of Computer Science, COMSATS University Islamabad Abbottabad Campus, Abbottabad 22060, Pakistan
2
Information Systems Department, Umm Al-Qura University, Makkah 21961, Saudi Arabia
3
Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
4
School of Information Technology, UNITAR International University, Kelana Jaya, Petaling Jaya 47301, Selangor, Malaysia
*
Authors to whom correspondence should be addressed.
Diagnostics 2023, 13(14), 2340; https://doi.org/10.3390/diagnostics13142340
Submission received: 15 May 2023 / Revised: 22 June 2023 / Accepted: 6 July 2023 / Published: 11 July 2023
(This article belongs to the Special Issue Artificial Intelligence in Medicine 2023)
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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.
Keywords: heart disease prediction; machine learning; reliable deep models; healthcare applications; distributed machine learning heart disease prediction; machine learning; reliable deep models; healthcare applications; distributed machine learning

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MDPI and ACS Style

Khan, M.A.; Alsulami, M.; Yaqoob, M.M.; Alsadie, D.; Saudagar, A.K.J.; AlKhathami, M.; Farooq Khattak, U. Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence. Diagnostics 2023, 13, 2340. https://doi.org/10.3390/diagnostics13142340

AMA Style

Khan MA, Alsulami M, Yaqoob MM, Alsadie D, Saudagar AKJ, AlKhathami M, Farooq Khattak U. Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence. Diagnostics. 2023; 13(14):2340. https://doi.org/10.3390/diagnostics13142340

Chicago/Turabian Style

Khan, Muhammad Amir, Musleh Alsulami, Muhammad Mateen Yaqoob, Deafallah Alsadie, Abdul Khader Jilani Saudagar, Mohammed AlKhathami, and Umar Farooq Khattak. 2023. "Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence" Diagnostics 13, no. 14: 2340. https://doi.org/10.3390/diagnostics13142340

APA Style

Khan, M. A., Alsulami, M., Yaqoob, M. M., Alsadie, D., Saudagar, A. K. J., AlKhathami, M., & Farooq Khattak, U. (2023). Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence. Diagnostics, 13(14), 2340. https://doi.org/10.3390/diagnostics13142340

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