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AuthorChkirbene Z.
AuthorMohamed A.
AuthorErbad A.
AuthorGuizani M.
Available date2022-04-21T08:58:25Z
Publication Date2020
Publication Name2020 International Wireless Communications and Mobile Computing, IWCMC 2020
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/IWCMC48107.2020.9148195
URIhttp://hdl.handle.net/10576/30094
AbstractSmart health systems improve the efficiency of healthcare infrastructures and biomedical systems by integrating information and technology into health and medical practices. However, reliability, scalabilty and latency are among the many challenges hindering the realization of next-generation healthcare. In fact, with the exponential increases in the volume of patient data being produced and processed, many healthcare system' are being overwhelmed with the deluge of data they are facing. Many systems have been proposed to improve the system latency and scalability. However, there are concerns related to some of theses systems regarding the increasing levels of required human interaction which impact their efficiency. Recently, machine learning techniques are gaining a lot of interest in health applications as they exhibit fast processing with realtime predictions. In this paper, we propose a new healthcare system to reduce the waiting time in emergency department and improve the network scalability in any distributed system. The proposed model integrates the power of edge computing with machine learning techniques for providing a good quality of healthcare services. The machine learning algorithm is used to generate a classifier that can predict with high levels of accuracy the likelyhood of a patient to have a heart attack using his physiological signals ECG. The proposed system stores the patient data in a centralized database and generates a unique index using a new data-dependent Indexing algorithm that transforms the patient data into unique code to be sent for any medical data exchange. Multiple machine learning algorithms are studied and the best algorithm will be selected based on efficient performance result for the prediction of heart attack problem. simulation results show that the proposed model can effectively detect the abnormal heart beats with 91% using SVM algorithm. We show also that the proposed system outperforms conventional indexing algorithm systems in terms of collisions rate. 2020 IEEE.
SponsorQatar National Research Fund
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectData Sharing
Distributed database systems
Efficiency
Electronic data interchange
Forecasting
Health care
Hospital data processing
Indexing (of information)
Machine learning
Medical information systems
Mobile computing
Scalability
Centralized data-base
Data sharing systems
Emergency departments
Healthcare infrastructure
Integrating information
Machine learning techniques
Physiological signals
Quality of health care
Learning algorithms
TitleSmart Edge Healthcare Data Sharing System
TypeConference Paper
Pagination577-582
dc.accessType Abstract Only


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