A Proposed Internet of Everything Framework for Disease Prediction

Authors

  • Noura E. Maghawry British University in Egypt - BUE
  • Samy Ghoniemy British University in Egypt - BUE

DOI:

https://doi.org/10.3991/ijoe.v15i04.9834

Keywords:

net of things, Internet of Everything, , bio-sensors, socio-sensors, Big data analyt-ics, Healthcare.

Abstract


Social networks and Internet of things are two paradigms when integrated a new paradigm Internet of Everything is established that has its impact on revolutionizing various fields such as engineering, industry and healthcare. Social networks became nowadays of the most important web services on which people heavily rely, thus became a major source for information extraction for rational decision making considering individuals as social or socio sensors. Furthermore, people using sensors especially biological sensors enabled the use of internet of things technology in building intelligent healthcare systems. One of the challenges facing the design of such systems is the design of an intelligent recommender system that is able to deal with such big data. For that, this paper proposes a framework to develop an enhanced intelligent expert advisor-based health monitoring and disease awareness system. The proposed framework enables the researchers to design advisory systems that are able to observe physiological signals through the use of different bio sensors and integrate it with historical medical data together with �  the massive data collected from social networks to provide accurate alerts and recommendations for many ailments inspected. The proposed Framework is designed to facilitate generic, dynamic and scalable process of integrating different types of social networks and bio sensors.

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Published

2019-02-27

How to Cite

Maghawry, N. E., & Ghoniemy, S. (2019). A Proposed Internet of Everything Framework for Disease Prediction. International Journal of Online and Biomedical Engineering (iJOE), 15(04), pp. 20–27. https://doi.org/10.3991/ijoe.v15i04.9834

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Section

Papers