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

  • Zaid Sako
  • Sasan Adibi
  • Nilmini Wickramasinghe
Part of the Healthcare Delivery in the Information Age book series (Healthcare Delivery Inform. Age)


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.


Data quality Data accuracy Information integrity Machine learning Diabetes 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zaid Sako
    • 1
  • Sasan Adibi
    • 2
  • Nilmini Wickramasinghe
    • 3
    • 4
  1. 1.Deakin UniversityKew EastAustralia
  2. 2.School of Information Technology, Deakin UniversityEast KewAustralia
  3. 3.Epworth HealthCareRichmondAustralia
  4. 4.Swinburne University of TechnologyHawthornAustralia

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