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Systematic map and review of predictive techniques in diabetes self-management

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  • EL Idrissi, Touria
  • Idri, Ali
  • Bakkoury, Zohra

Abstract

Data mining (DM) provides powerful tools to extract knowledge from large volumes of data offering valuable information to decision making. The extracted knowledge can be used for predictive and/or descriptive purposes. DM has been successfully used in different subfields of eHealth such us cardiology, oncology and endocrinology. This paper deals with the use of DM predictive techniques in diabetes self-management (DSM).To the best of our knowledge, neither a systematic map nor a systematic review have yet been performed with a focus on the use of DM predictive techniques in DSM. Thus, the aim of this study is to classify and review primary studies investigating DM predictive techniques in DSM by summarizing and analyzing knowledge with respect to: year and source of publication, type of diabetes, clinical tasks, DM predictive techniques, and the performance of the predictive techniques used. A total of 38 papers published between 2000 and April 2017 were therefore selected and analyzed accordingly to address six research questions. The results show that Type 1 Diabetes Mellitus (T1DM) is largely the type of diabetes that is most concerned by the studies and the prediction of blood glucose is the most investigated clinical task. Moreover, artificial neural networks were the most frequently used predictive technique which along with autogressive models, yield highest accuracy rates.

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  • EL Idrissi, Touria & Idri, Ali & Bakkoury, Zohra, 2019. "Systematic map and review of predictive techniques in diabetes self-management," International Journal of Information Management, Elsevier, vol. 46(C), pages 263-277.
    • Handle: RePEc:eee:ininma:v:46:y:2019:i:c:p:263-277
    DOI: 10.1016/j.ijinfomgt.2018.09.011
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    References listed on IDEAS

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    Cited by:

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    2. Khondaker Sazzadul Karim & Mohammad Ekramol Islam & Abdullah Mohammed Ibrahim & Shin-Hung Pan & Md. Mominur Rahman, 2023. "Online Marketing Trends and Purchasing Intent Advances in Customer Satisfaction through PLS-SEM and ANN Approach," Advances in Decision Sciences, Asia University, Taiwan, vol. 27(4), pages 24-54, December.
    3. A. R. Mohamed Yousuff & M. Zainulabedin Hasan & R. Anand & M. Rajasekhara Babu, 2024. "Leveraging deep learning models for continuous glucose monitoring and prediction in diabetes management: towards enhanced blood sugar control," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(6), pages 2077-2084, June.
    4. Nadine Ostern & Guido Perscheid & Caroline Reelitz & Jürgen Moormann, 2021. "Keeping pace with the healthcare transformation: a literature review and research agenda for a new decade of health information systems research," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(4), pages 901-921, December.

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