Cargando…
Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence
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 c...
Autores principales: | Khan, Muhammad Amir, Alsulami, Musleh, Yaqoob, Muhammad Mateen, Alsadie, Deafallah, Saudagar, Abdul Khader Jilani, AlKhathami, Mohammed, Farooq Khattak, Umar |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377760/ https://www.ncbi.nlm.nih.gov/pubmed/37510084 http://dx.doi.org/10.3390/diagnostics13142340 |
Ejemplares similares
-
Federated Machine Learning for Skin Lesion Diagnosis: An Asynchronous and Weighted Approach
por: Yaqoob, Muhammad Mateen, et al.
Publicado: (2023) -
A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease
por: Pachiyannan, Prabu, et al.
Publicado: (2023) -
WEENet: An Intelligent System for Diagnosing COVID-19 and Lung Cancer in IoMT Environments
por: Muhammad, Khan, et al.
Publicado: (2022) -
A Novel Benchmark Dataset for COVID-19 Detection during Third Wave in Pakistan
por: Jalil, Zunera, et al.
Publicado: (2022) -
Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification
por: Ullah, Waseem, et al.
Publicado: (2022)