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

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

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.

Keywords

Systematic mapping study Data mining Diabetes Self-management 
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Notes

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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Software Project Management Research Team, RITCENSIAS, University Mohamed VRabatMorocco
  2. 2.Department of Computer Sciences EMIUniversity Mohamed VRabatMorocco

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