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A Data Analytics Suite for Exploratory Predictive, and Visual Analysis of Type 2 Diabetes

journal contribution
posted on 2023-08-30, 19:30 authored by Nada Philip, Manzoor Razaak, John Chang, Maurice O'Kane, Barbara K. Pierscionek
Long-term management of chronic disorders such as Type 2 Diabetes (T2D) requires personalised care for patients due to variation in patient characteristics and their response to a specific line of treatment. The availability of large volumes of electronic records of T2D patient data provides opportunities for application of big data analysis to gain insights into the disease manifestation and its impact on patients. Data science in healthcare has the potential to identify hidden knowledge from the database, re-confirm existing knowledge, and aid in personalising treatment. In this paper, we present a suite of data analytics for T2D disease management that allows clinicians and researchers to identify associations between different patient biological markers and T2D related complications. The analytics suite consists of exploratory, predictive, and visual analytics with capabilities including multi-tier classification of T2D patient profiles that associate them to specific conditions, T2D related complication risk prediction, and prediction of patient response to a particular line of treatment. The analytics presented in this paper explore advanced data analysis techniques, which are potential tools for clinicians in decision-making that can contribute to better management of T2D.

History

Refereed

  • Yes

Volume

10

Page range

13460-13471

Publication title

IEEE Access

ISSN

2169-3536

Publisher

IEEE

File version

  • Accepted version

Language

  • eng

Legacy posted date

2022-01-17

Legacy creation date

2022-01-17

Legacy Faculty/School/Department

Faculty of Health, Education, Medicine & Social Care

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