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Data Mining Techniques in Diabetes Self-management: A Systematic Map

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Book cover Trends and Advances in Information Systems and Technologies (WorldCIST'18 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 746))

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Abstract

Data mining techniques (DMT) provide powerful tools to extract knowledge from data helping in decision making. Medicine, like many other fields, is using DM in diabetes, cardiology, cancer and other diseases. In this paper, we investigate the use of DMT in diabetes, in particular in diabetes self-management (DSM). The purpose is to conduct a systematic mapping study to review primary studies investigating DMT in DSM. This mapping study aims to summarize and analyze knowledge on: (1) years and sources of DSM publications, (2) type of diabetes that took most attention, (3) DM tasks and techniques most frequently used, and (4) the considered functionalities. A total of 57 papers published between 2000 and April 2017 were selected and analyzed regarding four research questions. The study shows that prediction was largely the most used DM task and Neural Networks were the most frequently used technique. Moreover, T1DM is largely the type of diabetes that is most concerned by the studies so as the Prediction of blood glucose.

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Acknowledgment

This research is part of the project “mPHR in Morocco” financed by the Ministry of High education and Scientific research in Morocco 2015-2017.

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Authors and Affiliations

  1. Software Project Management Research Team, RITC, ENSIAS, University Mohamed V, Rabat, Morocco

    Touria El Idrissi & Ali Idri

  2. Department of Computer Sciences EMI, University Mohamed V, Rabat, Morocco

    Zohra Bakkoury

Authors
  1. Touria El Idrissi
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  2. Ali Idri
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  3. Zohra Bakkoury
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Corresponding author

Correspondence to Ali Idri .

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Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, Ohio, USA

    Hojjat Adeli

  3. DSI/EEUM, Universidade do Minho, Guimarães, Portugal

    Luís Paulo Reis

  4. DIMES, Universita della Calabria, Arcavacata di Rende, Italy

    Sandra Costanzo

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El Idrissi, T., Idri, A., Bakkoury, Z. (2018). Data Mining Techniques in Diabetes Self-management: A Systematic Map. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-319-77712-2_109

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  • DOI: https://doi.org/10.1007/978-3-319-77712-2_109

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77711-5

  • Online ISBN: 978-3-319-77712-2

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