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 :
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