Addressing Data Accuracy and Information Integrity in mHealth Solutions Using Machine Learning Algorithms

Sako, Zaid, Adibi, Sasan and Wickramasinghe, Nilmini 2020, Addressing Data Accuracy and Information Integrity in mHealth Solutions Using Machine Learning Algorithms. In Wickramasinghe, Nilmini and Bodendorf, F (ed), Delivering Superior Health and Wellness Management with IoT and Analytics, Springer, Cham, Switzerland, pp.345-359, doi: 10.1007/978-3-030-17347-0_16.

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Title Addressing Data Accuracy and Information Integrity in mHealth Solutions Using Machine Learning Algorithms
Author(s) Sako, Zaid
Adibi, SasanORCID iD for Adibi, Sasan orcid.org/0000-0002-3868-6269
Wickramasinghe, NilminiORCID iD for Wickramasinghe, Nilmini orcid.org/0000-0002-1314-8843
Title of book Delivering Superior Health and Wellness Management with IoT and Analytics
Editor(s) Wickramasinghe, NilminiORCID iD for Wickramasinghe, Nilmini orcid.org/0000-0002-1314-8843
Bodendorf, F
Publication date 2020
Series Healthcare Delivery in the Information Age
Chapter number 16
Total chapters 31
Start page 345
End page 359
Total pages 15
Publisher Springer
Place of Publication Cham, Switzerland
Keyword(s) Health Care Sciences & Services
Health Policy & Services
HEALTH-CARE
Life Sciences & Biomedicine
Medical Informatics
OMAHA SYSTEM
Science & Technology
Summary Today, much of the healthcare delivery is done digitally. In particular, there exists a plethora of mHealth solutions being developed. This in turn necessitates the need for accurate data and information integrity if superior mHealth is to ensue. Lack of data accuracy and information integrity can cause serious harm to patients and limit the benefits of mHealth technology. The described exploratory case study serves to investigate data accuracy and information integrity in mHealth, with the aim of incorporating Machine Learning to detect sources of inaccurate data and deliver quality information. The outcome of the study was a successful testing of a Machine Learning algorithm (Decision Tree) for mHealth data that consisted of secondary diabetes data. The algorithm was able to classify the data as accurate or inaccurate.
ISBN 9783030173463
Language eng
DOI 10.1007/978-3-030-17347-0_16
HERDC Research category B1 Book chapter
Persistent URL http://hdl.handle.net/10536/DRO/DU:30156399

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