DLRT: Deep learning approach for reliable diabetic treatment

Heena Rathore, Abdulla Al-Ali, Amr Mohamed, Xiaojiang Du, Mohsen Guizani

Research output: Contribution to journalConference articlepeer-review

15 Scopus citations

Abstract

Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today, instead of physically utilizing syringes for infusing insulin, a patient can utilize a gadget, for example, a Wireless Insulin Pump (WIP) to pass insulin into the body. A typical WIP framework comprises of an insulin pump, continuous glucose management system, blood glucose monitor, and other associated devices with all connected wireless links. This takes into consideration more granular insulin conveyance while achieving blood glucose control. WIP frameworks have progressively benefited patients, yet the multifaceted nature of the subsequent framework has posed in parallel certain security implications. This paper proposes a highly accurate yet efficient deep learning methodology to protect these vulnerable devices against fake glucose dosage. Moreover, the proposal estimates the reliability of the framework through the Bayesian network. We conduct comparative study to conclude that the proposed method outperforms the state of the art by over 15% in accuracy achieving more than 93% accuracy. In addition, the proposed approach enhances the reliability of the overall system by 18% when only one wireless link is secured, and more than 90% when all wireless links are secured.

Original languageEnglish
Pages (from-to)1-6
Number of pages6
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
Volume2018-January
DOIs
StatePublished - 2017
Event2017 IEEE Global Communications Conference, GLOBECOM 2017 - Singapore, Singapore
Duration: 4 Dec 20178 Dec 2017

Keywords

  • Deep learning
  • Implantable medical devices
  • Insulin pumps
  • Machine learning
  • Security

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