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A Proposed Architecture for Predicting Breast Cancer using Fog Computing

Laila A. Abd-Elmegid. Published in Data Mining.

Communications on Applied Electronics
Year of Publication: 2019
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Laila A. Abd-Elmegid

Laila A Abd-Elmegid. A Proposed Architecture for Predicting Breast Cancer using Fog Computing. Communications on Applied Electronics 7(26):32-35, February 2019. BibTeX

	author = {Laila A. Abd-Elmegid},
	title = {A Proposed Architecture for Predicting Breast Cancer using Fog Computing},
	journal = {Communications on Applied Electronics},
	issue_date = {February 2019},
	volume = {7},
	number = {26},
	month = {Feb},
	year = {2019},
	issn = {2394-4714},
	pages = {32-35},
	numpages = {4},
	url = {},
	doi = {10.5120/cae2019652815},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


A new emerged concept, known as fog computing, aims to local processing and storage of large amounts of data at its sources. Such concept differs from the centralized style of processing and storage used with the cloud computing concept. Fog computing technology improves both the performance and the efficiency. The main cause of advantages gained from fog computing is the reduced amount of transferred data to the cloud need to be analyzed, processed, and stored. little research has been dedicated to study how to efficiently apply big data analytics locally where data has been collected and stored. This study proposes a fog computing-based architecture for prediction of the breast cancer prognosis. The proposed architecture uses the BCOAP model for the prediction purpose. It addresses the problem of real time processing of large amounts of data without overwhelming the data center at the cloud. It distinguishes between the processes to be performed on the cloud and the others to be performed on the fog. Initial prototype of the proposed architecture has highlighted its efficiency and how it increases the potentials of the applied BCOAP model.


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Breast cancer, BCOAP, Classification, Cloud computing, Fog computing.