Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence

• DOI: 10.3390/diagnostics13142340
• Publication Date: 2023-07-12T05:01:41Z
• Authors: Muhammad Amir Khan; Musleh Alsulami; Muhammad Mateen Y...; Deafallah Alsadie; Abdul Khader Jila...; Mohammed AlKhathami; Umar Farooq Khattak
• Keywords: distributed machine learning; Medicine (General); healthcare applications; 02 engineering and technology; Article; 3. Good health; heart disease prediction; machine learning; reliable deep models; healthcare applications; distributed machine learning; reliable deep models; machine learning; R5-920; 0202 electrical engineering, electronic engineering, information engineering; heart disease prediction

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.