Prediction of Chronic Kidney Disease using Data Mining Techniques |
Author(s): |
| Shyma J P , Vishveshvaraya Technological University Karnataka; Sphoorti R Kurthukoti, NIE IT Mysuru; Fathima Nimra Khan, NIE IT Mysuru; Sheeba Zain R, NIE IT Mysuru; Smt Neelaja K., NIE IT Mysuru |
Keywords: |
| GFR - Glomerular Filtration Rate, Data Mining |
Abstract |
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Our work predominantly focuses on detecting chronic kidney disease using Classification algorithms like Naive Bayes and one of the major contribution of this paper work is prediction of the stages to which that particular patient is subjected, there are basically 5 stages whose calculation is mainly depended on one of the major factor GFR - glomerular filtration rate. This GFR - glomerular filtration rate is the best test to measure a patient's level of kidney function and determine stage of kidney disease. In clinical practice, however, creatinine clearance or estimates of creatinine clearance based on the serum creatinine level are used to measure GFR. |
Other Details |
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Paper ID: IJSRDV6I30979 Published in: Volume : 6, Issue : 3 Publication Date: 01/06/2018 Page(s): 2303-2306 |
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