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Addressing data accuracy and information integrity in mHealth using ML

Sako, Zaid Zekiria 2019, Addressing data accuracy and information integrity in mHealth using ML, MAppSci thesis, School of Health and Social Development, Deakin University.

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sako-addressingdata-2018.pdf Connect to thesis application/pdf 5.44MB 117

Title Addressing data accuracy and information integrity in mHealth using ML
Author Sako, Zaid Zekiria
Institution Deakin University
School School of Health and Social Development
Faculty Faculty of Health
Degree type Research masters
Degree name MAppSci
Date submitted 2019-02-20
Summary The aim of the study was finding a way in which Machine Learning can be applied in mHealth Solutions to detect inaccurate data that can potentially harm patients. The result was an algorithm that classified accurate and inaccurate data.
Language eng
Indigenous content off
Field of Research 080702 Health Informatics
Socio Economic Objective 929999 Health not elsewhere classified
Description of original 167 p.
Copyright notice ©The author
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30129033

Document type: Thesis
Collections: Faculty of Health
School of Health and Social Development
Higher degree theses (Open Access)
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

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Access Statistics: 219 Abstract Views, 122 File Downloads  -  Detailed Statistics
Created: Tue, 20 Aug 2019, 14:33:55 EST by Bec Miller

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.