• DocumentCode
    667284
  • Title

    Short-term vs. long-term analysis of diabetes data: Application of machine learning and data mining techniques

  • Author

    Georga, Eleni I. ; Protopappas, Vasilios C. ; Mougiakakou, Stavroula G. ; Fotiadis, Dimitrios I.

  • Author_Institution
    Dept. of Mater. Sci. & Eng., Univ. of Ioannina, Ioannina, Greece
  • fYear
    2013
  • fDate
    10-13 Nov. 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Chronic care of diabetes comes with large amounts of data concerning the self- and clinical management of the disease. In this paper, we propose to treat that information from two different perspectives. Firstly, a predictive model of short-term glucose homeostasis relying on machine learning is presented with the aim of preventing hypoglycemic events and prolonged hyperglycemia on a daily basis. Second, data mining approaches are proposed as a tool for explaining and predicting the long-term glucose control and the incidence of diabetic complications.
  • Keywords
    data analysis; data mining; diseases; learning (artificial intelligence); medical computing; sugar; chronic diabetes care; data mining technique; disease clinical management; disease self-management; hypoglycemic event prevention; long-term data analysis; long-term glucose control prediction; machine learning technique; prolonged hyperglycemia prevention; short-term data analysis; short-term glucose homeostasis; Data mining; Diabetes; Insulin; Monitoring; Predictive models; Sugar;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on
  • Conference_Location
    Chania
  • Type

    conf

  • DOI
    10.1109/BIBE.2013.6701622
  • Filename
    6701622