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

Philip, Nada, Razaak, Manzoor, Chang, John, O'Kane, Maurice and Pierscionek, Barbara K. (2022) A Data Analytics Suite for Exploratory Predictive, and Visual Analysis of Type 2 Diabetes. IEEE Access, 10. pp. 13460-13471. ISSN 2169-3536

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Official URL: http://dx.doi.org/10.1109/ACCESS.2022.3146884

Abstract

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.

Item Type: Journal Article
Keywords: Big data for healthcare, data analytics, personalized care, healthcare data visualisation, prediction analytics, risk prediction, T2D
Faculty: Faculty of Health, Education, Medicine & Social Care
Depositing User: Ian Walker
Date Deposited: 17 Jan 2022 14:37
Last Modified: 13 Jun 2022 13:56
URI: https://arro.anglia.ac.uk/id/eprint/707257

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