IDEAS home Printed from https://ideas.repec.org/a/ids/ijient/v6y2019i2-3-4p204-216.html
   My bibliography  Save this article

Architecture of a decision support system based on big data for monitoring type 2 diabetics

Author

Listed:
  • Boudhir Anouar Abdelhakim
  • Ben Ahmed Mohamed
  • Fellaji Soumaya

Abstract

Type 2 diabetes is one of chronic diseases that require continuous and real-time monitoring to prevent the occurrence of complications. First, the doctor must have information about the patient's daily life (vital signs, stress, sedentary lifestyle, physical activities, nutrition, etc.). Secondly, the prescribed treatment must be evaluated each time to test the validity of the diagnosis. To achieve this goal, a decision support system based on big data mining technology must be designed in order to have a centralised knowledge of diabetics. This system will improve the quality of monitoring and treatment from the different data collected. Thus, this paper presents an architecture of a decision support system allowing doctors to monitor the health status of their patients, based on data collected from different resources, in order to enrich the knowledge database and prescribe new treatments based on similar cases and experiences of doctors and patients belonging to this system.

Suggested Citation

  • Boudhir Anouar Abdelhakim & Ben Ahmed Mohamed & Fellaji Soumaya, 2019. "Architecture of a decision support system based on big data for monitoring type 2 diabetics," International Journal of Intelligent Enterprise, Inderscience Enterprises Ltd, vol. 6(2/3/4), pages 204-216.
    • Handle: RePEc:ids:ijient:v:6:y:2019:i:2/3/4:p:204-216
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=101127
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijient:v:6:y:2019:i:2/3/4:p:204-216. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=167 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.