Name
Title | ||
|---|---|---|
Short-Term Vs. Long-Term Analysis Of Diabetes Data: Application Of Machine Learning And Data Mining Techniques |
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/BIBE.2013.6701622 | 2013 IEEE 13TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE) |
Keywords | Field | DocType |
data analysis,learning artificial intelligence,data mining | Data science,Data mining,Diabetes mellitus,Disease,Computer science,Chronic care,Artificial intelligence,Machine learning | Conference |
ISSN | Citations | PageRank |
2471-7819 | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eleni I. Georga | 1 | 30 | 6.48 |
| Vasilios C. Protopappas | 2 | 15 | 2.12 |
| Stavroula G Mougiakakou | 3 | 342 | 28.61 |
| Dimitrios I. Fotiadis | 4 | 941 | 121.32 |