Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence

Autor:	Muhammad Amir Khan, Musleh Alsulami, Muhammad Mateen Yaqoob, Deafallah Alsadie, Abdul Khader Jilani Saudagar, Mohammed AlKhathami, Umar Farooq Khattak
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	heart disease prediction machine learning reliable deep models healthcare applications distributed machine learning Medicine (General) R5-920
Zdroj:	Diagnostics, Vol 13, Iss 14, p 2340 (2023)
Druh dokumentu:	article
ISSN:	2075-4418
DOI:	10.3390/diagnostics13142340
Popis:	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:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/d4651e96c25145ce8c5a5d66b3a72398 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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