Data Mining Techniques to Predict Diabetes Influenced Kidney Disease

Authors(3) :-Swaroopa Shastri, Surekha, Sarita

The 18 to 20% adults above than the age of 65 are being getting distressed globally that of the type 2 Diabetes Mellitus (DM). Disease named as Diabetic Kidney Disease (DKD), which is the recurrent as well as unsafe difficulty of DM2 that has influenced over the 1/3 of the DM2 patients. The DKD patients are exposed to the high threat of having together the hyperglycemia as well as hypoglycemia, where the glucose homeostasis in such patients is enormously varied. The patient with both the high as well as low glycemic levels will be facing the amplified morbidity also the continued existence is condensed. The patients with DKD have a chance of the aspects in which the hazard of the hypoglycemia is bigger, they are gluconeogenesis of renal is lessened, the pathways of the metabolic are disturbed plus the insulin consent is declined. The examining along with this the controlling of the glycemic at the apt point in favor of the diabetic patients is necessitated so as to be away from the hypoglycemia as well as the added disarrays of glycemic to the patients who have DM2 plus the Kidney disease. On behalf of treating the diabetic patients the bodily processes of the renal and the DKD patho-physiology have turn out to be the largest part as an imperative perceptive for the dedicated expertises.

Authors and Affiliations

Swaroopa Shastri
Department of Studies in Computer Application (MCA) VTU Center for PG Studies Kalaburgi Kalaburgi, India
Surekha
Department of Studies in Computer Application (MCA) VTU Center for PG Studies Kalaburgi Kalaburgi, India
Sarita
Department of Studies in Computer Application (MCA) VTU Center for PG Studies Kalaburgi Kalaburgi, India

DKD, hypoglycemia, Diabetic Kidney Disease, CNS, SGLT-2

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Publication Details

Published in : Volume 2 | Issue 4 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 364-368
Manuscript Number : CSEIT172494
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Swaroopa Shastri, Surekha, Sarita, "Data Mining Techniques to Predict Diabetes Influenced Kidney Disease", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.364-368, July-August-2017.
Journal URL : http://ijsrcseit.com/CSEIT172494

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