Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence
Muhammad Amir Khan,
Musleh Alsulami,
Muhammad Mateen Yaqoob,
Deafallah Alsadie,
Abdul Khader Jilani Saudagar,
Mohammed AlKhathami,
Umar Farooq Khattak
Affiliations
Muhammad Amir Khan
Department of Computer Science, COMSATS University Islamabad Abbottabad Campus, Abbottabad 22060, Pakistan
Musleh Alsulami
Information Systems Department, Umm Al-Qura University, Makkah 21961, Saudi Arabia
Muhammad Mateen Yaqoob
Department of Computer Science, COMSATS University Islamabad Abbottabad Campus, Abbottabad 22060, Pakistan
Deafallah Alsadie
Information Systems Department, Umm Al-Qura University, Makkah 21961, Saudi Arabia
Abdul Khader Jilani Saudagar
Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Mohammed AlKhathami
Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Umar Farooq Khattak
School of Information Technology, UNITAR International University, Kelana Jaya, Petaling Jaya 47301, Selangor, Malaysia
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.