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
Link To Document